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Journal articles on the topic 'F1-score and accuracy'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Rahmawan, Fachrudin Okta, Hanafi, and Windha Mega Pradnya Dhuita. "Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments." Teknika 14, no. 1 (2025): 26–33. https://doi.org/10.34148/teknika.v14i1.1117.

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This study aims to evaluate the accuracy of two algorithms, K-Nearest Neighbor (KNN) and Robustly Optimized BERT Approach (RoBERTa), in analyzing sentiment within comments on MiawAug’s YouTube channel. Sentiment analysis was conducted on two sentiment categories: binary classification (positive and negative) and multi-class classification (positive, neutral, and negative). Using KNN, the binary classification yielded an accuracy of 86.12%, F1-score of 87.44%, recall of 96.64%, and precision of 79.89%. In contrast, the multi-class classification achieved 98.21% accuracy, F1-score, and recall wi

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Soni, Ekta, Arpita Nagpal, and Sujata Bhutani. "Automatic ECG Arrhythmia Recognition using ANN and CNN." International Journal of Experimental Research and Review 45, Spl Vol (2024): 01–14. https://doi.org/10.52756/ijerr.2024.v45spl.001.

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Present research highlights the need for more patient-oriented monitoring systems for cardiac health, especially in the aftermath of COVID-19. The study introduces a contactless and affordable ECG device capable of recording heart arrhythmias for remote monitoring, which is vital in managing the rising incidence of untimely heart attacks. Two deep learning algorithms have been developed to design the system: RCANN (Real-time Compressed Artificial Neural Network) and RCCNN (Real-time Compressed Convolutional Neural Network), respectively, based on ANN and CNN. These methods are designed to clas

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A.Harshavardhan. "Optimal Routing in Wireless Sensor Networks for Advancing IoT Efficiency and Sustainability using Enhanced Ant Colony Algorithm with machine learning approaches." Journal of Electrical Systems 20, no. 2s (2024): 922–30. http://dx.doi.org/10.52783/jes.1689.

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This research study aims to investigate the incorporation of machine learning tools, such as Q-learning, Genetic Algorithms, Unsupervised Learning, and Ensemble Learning, into Enhanced Ant Colony Algorithm to assess the impacts of such incorporation on the WSN’s performance. Ten experimental trials were conducted on each to analyze the accuracy, precision, and F1 score results. It was observed that Q-learning achieves an average accuracy of 0.867; precision of 0.842; and F1 score of 0.854, making it highly adaptable and efficient in making routing decisions. The GA presented average accuracy o

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Ramadan, Muhamad Firly, Martanto, Arif Rinaldi Dikananda, and Ahmad Rifa'i. "Comparison of Sentiment Analysis Models Enhanced by Naïve Bayes and Support Vector Machine Algorithms on Mobile Banking BRImo Reviews." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 677–86. https://doi.org/10.59934/jaiea.v4i2.732.

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This study compares the effectiveness of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying user sentiment regarding the BRImo application. User reviews were obtained from the Google Play Store platform and underwent a text preprocessing stage to clean and prepare the data. Subsequently, the SVM and Naïve Bayes algorithms were applied for sentiment analysis, using evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that SVM achieved a training accuracy of 95.67% and a testing accuracy of 83.11%, with its best performance on positive se

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Adhitya, Rahmat Ryan, Wina Witanti, and Rezki Yuniarti. "PERBANDINGAN METODE CART DAN NAÏVE BAYES UNTUK KLASIFIKASI CUSTOMER CHURN." INFOTECH journal 9, no. 2 (2023): 307–18. http://dx.doi.org/10.31949/infotech.v9i2.5641.

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Classification is the process of identifying and grouping an object into the same group or category Classification can be used to group a large-sized dataset, and some commonly used classification methods are CART (Classification And Regression Tree) and Naïve Bayes. This study discusses the comparison of CART and Naïve Bayes methods by measuring accuracy, precision, recall, and f1-score values with 3 scenarios of training and testing dataset distribution. Accuracy, precision, recall, and f1-score measurements are performed using a confusion matrix. The scenarios for training and testing datas

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Mahmud, Fuad, Badruddowza Badruddowza, Md Shohail Uddin Sarker, et al. "ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION." American Journal of Engineering and Technology 06, no. 10 (2024): 86–99. http://dx.doi.org/10.37547/tajet/volume06issue10-10.

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This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model fo

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Kim, Seong-Jin, Xue-Cheng Jin, Rajaraman Bharanidharan, and Na-Yeon Kim. "Monitoring Multiple Behaviors in Beef Calves Raised in Cow–Calf Contact Systems Using a Machine Learning Approach." Animals 14, no. 22 (2024): 3278. http://dx.doi.org/10.3390/ani14223278.

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The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acce

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Inda, Nur. "Analisis Perbandingan Kinerja Model Yolov7 dalam Deteksi Kuku Diabetes." Journal of System and Computer Engineering (JSCE) 5, no. 2 (2024): 226–36. http://dx.doi.org/10.61628/jsce.v5i2.1334.

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abstrak Diabetes mellitus (DM) penyakit degenerative dan tidak menular yang dapat dilihat dari warna kuku jari tangan. Dalam menganalisis warna mata manusia memiliki keterbatasan dalam pengenalan warna dan analisis tekstur sedangkan komputer mampu mengklasifikasi jutaan warna dan sedikit perubahan tekstur untuk mengenali perubahan warna kuku individu untuk mencega gejala awal diabetes menggunakan metode YOLOv7 untuk mewakili model satu tahap untuk mendeteksi objek menggunakan Convolutional Neural Network (CNN). Penelitian ini dilaksanakan di Puskesmas Polewali. Pengambilan sampel dilakukan den

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Wirawan, Bima Mahardika, Mahendra Dwifebri Purbolaksono, and Fhira Nhita. "Handling Unbalanced Data Sets Using DBMUTE and NearMiss Methods to Improve Classification Performance of Yeast Data Sets." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 3 (2023): 1062. http://dx.doi.org/10.30865/mib.v7i3.6306.

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Yeast vacuole biogenesis was chosen as a model system for organelle assembly because most vacuole functions can be used for vegetative cell growth. Therefore it is possible to generate an extensive collection of mutants with defects in unbalanced vacuole assembly. With this in mind, we must find the structural balance of data in yeast. Imbalanced data is when there is an unbalanced distribution of data classes and the number of data classes is either more or lower than the number of other data classes. Our method uses the f1score performance matrix method and the balanced accuracy on DBMUTE an

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Chetry, Mandika, Ruiling Feng, Samra Babar, et al. "Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes." PeerJ Computer Science 11 (April 24, 2025): e2784. https://doi.org/10.7717/peerj-cs.2784.

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Early detection of breast cancer (BC) is essential for effective treatment and improved prognosis. This study compares the performance of various machine learning (ML) algorithms, including convolutional neural networks (CNNs), logistic regression (LR), support vector machines (SVMs), and Gaussian naive Bayes (GNB), on two key datasets, Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Histopathological Image Classification (BreaKHis). For the BreaKHis dataset, the CNN achieved an impressive accuracy of 92%, with precision, recall, and F1 score values of 91%, 93%, and 91%, respective

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Feng, Yuan. "Research on Victim Genders in LA based-on Machine Learning." Highlights in Business, Economics and Management 24 (January 22, 2024): 764–69. http://dx.doi.org/10.54097/ggrkeb45.

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Recent years, more and more victims in crime in Los Angeles, especially after COVID-19. And women have more fear of crime and feel that they are more vulnerable than men. This article studies the future victim gender prediction by using algorithms in machine learning, like KNN, logistic regression and neural network. Machine learning has become more and more popular in the past few years. The results show that females are not seem as more vulnerable in crime than males in LA region. In addition, neural network method performs better than KNN and logistic regression, showing higher accuracy and

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Munsch, Nicolas, Alistair Martin, Stefanie Gruarin, et al. "Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study." Journal of Medical Internet Research 22, no. 10 (2020): e21299. http://dx.doi.org/10.2196/21299.

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Background A large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner. Objective The aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers. Methods We identified 10 web-based COVID-19 symptom checkers, all of which were included in the study. We evaluated the COVID-19 symptom checkers by assessing 50 COVID-19 case

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Bimawan, Zaynuri Ilham, Tri Astuti, and Primandani Arsi. "COMPARISON OF RANDOM FOREST, K-NEAREST NEIGHBOR, DECISION TREE, AND XGBOOST ALGORITHMS FOR DETECTING STUNTING IN TODDLERS." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1599–607. https://doi.org/10.52436/1.jutif.2024.5.6.2629.

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Stunting is a significant health issue in many developing countries, including Indonesia. Advances in health technology have opened new opportunities to improve the accuracy and efficiency of detecting stunting in young children, with one such advancement being Machine Learning technology. This study compares various Machine Learning algorithms for detecting stunting in children. The methodology includes data collection, data exploration, data preprocessing, feature extraction, model classification, and model evaluation. The results show that Random Forest demonstrates superior performance wit

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Muhammad Daffa Al Fahreza, Ardytha Luthfiarta, Muhammad Rafid, and Michael Indrawan. "Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z." Journal of Applied Computer Science and Technology 5, no. 1 (2024): 16–25. http://dx.doi.org/10.52158/jacost.v5i1.715.

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Mental health is a significant concern in society today, particularly for Generation Z, who are vulnerable to experiencing mental health problems that can disrupt daily productivity. The influence of working hours also contributes to the mental health of this generation. To assess public opinion on this issue, sentiment analysis is needed on social media, especially twitter. This research uses the Gaussian Naïve Bayes algorithm and Support Vector Machine with various stemming algorithms such as Nazief-Adriani, Arifin Setiono, and Sastrawi. The sentiment analysis method is used to assess positi

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Ungkawa, Uung, and Muhammad Avilla Rafi. "Data Balancing Techniques Using the PCA-KMeans and ADASYN for Possible Stroke Disease Cases." Jurnal Online Informatika 9, no. 1 (2024): 138–47. http://dx.doi.org/10.15575/join.v9i1.1293.

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Imbalanced data happens when the distribution of classes is not equal between positive and negative classes. In healthcare, the majority class typically consists of healthy patient data, while the minority class contains sick patient data. This condition can cause the minority class prediction to be wrong because the model tends to predict the majority class. In this study, we use a deep neural network algorithm with focal loss that can deal with class imbalance during training. To balance the data, we use the PCA-KMeans combination model to shrink the dataset and the ADASYN model to give the

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Shannaq, Boumedyen, Oualid Ali, Said Al Maqbali, and Afraa Al-Zeidi. "Advancing user classification models: A comparative analysis of machine learning approaches to enhance faculty password policies at the University of Buraimi." Journal of Infrastructure, Policy and Development 8, no. 13 (2024): 9311. http://dx.doi.org/10.24294/jipd9311.

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In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit

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POLLY, Yulianto Triwahyuadi, Adriana FANGGIDAE, Juan Rizky Mannuel LEDOH, Clarissa Elfira AMOS PAH, Bertha S. DJAHI, and Kisan Emiliano Rape TUPEN. "A new approach for diabetes risk detection using quadratic interpolation flower pollination neural network." Applied Computer Science 21, no. 2 (2025): 63–81. https://doi.org/10.35784/acs_7186.

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This study aims to evaluate and compare five algorithms in diabetes detection, namely Flower Pollination Neural Network (FPNN), Particle Swarm Optimization Neural Network (PSONN), Bat Artificial Neural Network (BANN), Stochastic Gradient Descent (SGD), and Quadratic Interpolation Flower Pollination Neural Network (QIFPNN). These algorithms were tested on a diabetes risk dataset divided into training, validation, and testing subsets. The evaluation was based on three main aspects: accuracy, F1 score, and training time. Experimental results showed that QIFPNN outperformed others with an average

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Kumar, Kanakala SS Praveen. "Machine Learning-Powered Fraud App Detection: Safeguarding Google Play Store Integrity." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1608–13. http://dx.doi.org/10.22214/ijraset.2024.63378.

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Abstract: As the variety of mobile applications used in daily life expands, it becomes crucial to stay updated and discern which apps are safe and which are not. It is challenging to make a judgment. Our methodology predicts using four criteria: ratings, feedback, in-app purchases, and the presence of advertisements. The system assesses three models: Naïve Bayes, logistic regression, and decision tree classifier. These models were then evaluated based on four F1 score metrics: recall, precision, and accuracy. A high F1 score should exceed 0.7, and a recall score greater than 0.5 indicates enha

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Nasrullah, Muhammad, Bayu Surarso, and Oky Dwi Nurhayati. "Analysis of Naïve Bayes and K-Nearest Neighbors Algorithms for Classifying Fishermen Aid Eligibility." Jurnal Penelitian Pendidikan IPA 10, no. 10 (2024): 7652–64. http://dx.doi.org/10.29303/jppipa.v10i10.8818.

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This article analyzes the use of data mining with Naïve Bayes and K-Nearest Neighbor (KNN) algorithms to build classification models and evaluate their performance in identifying fishermen eligible for aid. The study aims to compare the effectiveness of these algorithms in handling imbalanced datasets using the Synthetic Minority Over-sampling Technique (SMOTE). The research applies SMOTE to improve the balance of the dataset before classification. Without SMOTE, Naïve Bayes achieved an accuracy of 97.01%, precision of 94.16%, recall of 96.67%, and F1-score of 95.39%. KNN, on the other hand, r

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Arsad, Alva Hendi Muhammad, and Tonny Hidayat. "Classification of Mental Disorders Using Modified Balanced Random Forest And Feature Selection." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 9, no. 2 (2024): 45–54. http://dx.doi.org/10.20527/jtiulm.v9i2.320.

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This study employs the Modified Balanced Random Forest (MBRF) algorithm and Correlation-based Feature Selector (CfsSubsetEval) for mental disorder classification. The "Mental Disorder Classification" dataset from Kaggle was used with the aim of improving accuracy, evaluating feature selection, and assessing MBRF's performance in handling data imbalance. The study compares the performance of Random Forest (RF) and MBRF, and examines the impact of feature selection using CFS on mental disorder classification. The results indicate that MBRF outperforms RF with an 8.33% improvement in accuracy, 8.

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Adiputra, I. Nyoman Mahayasa, Pei-Chun Lin, and Paweena Wanchai. "The Effectiveness of Generative Adversarial Network-Based Oversampling Methods for Imbalanced Multi-Class Credit Score Classification." Electronics 14, no. 4 (2025): 697. https://doi.org/10.3390/electronics14040697.

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Credit score models are essential tools for evaluating creditworthiness and mitigating financial risks. However, the imbalanced nature of multi-class credit score datasets poses significant challenges for traditional classification algorithms, leading to poor performance in minority classes. This study explores the effectiveness of Generative Adversarial Network (GAN)-based oversampling methods, including CTGAN, CopulaGAN, WGAN-GP, and DraGAN, in addressing this issue. By synthesizing realistic data for minority classes and integrating it with majority class data, the study benchmarks these GA

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Oladipupo, Samuel Adegbite, Stephen Olatunde Olabiyisi, Wasiu Oladimeji Ismaila, Adebayo Olalere Oyedele, and Oluwatobi Joel Toyobo. "Comparative Performance Analysis of Selected Machine Learning Techniques for Social Media Sentiment Analysis." International Journal of Research and Innovation in Applied Science X, no. V (2025): 601–7. https://doi.org/10.51584/ijrias.2025.100500055.

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Social media sentiment analysis plays a crucial role in understanding public opinion and user behavior across platforms. Several techniques have been developed to accurately classify sentiment in social media data. However, these techniques have not been adequately analyzed and compared. Hence, this study investigates the comparative performance of Support Vector Machine (SVM), Logistic Regression (LR) and Long Short-Term Memory (LSTM) in social media sentiment analysis. Social media data which contains labelled tweet representing different sentiments (positive, negative, neutral) were extract

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Banou, Z., S. Elfilali, and H. Benlahmar. "Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis." Mathematical Modeling and Computing 10, no. 2 (2023): 511–17. http://dx.doi.org/10.23939/mmc2023.02.511.

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Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text. Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score. However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming. In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange polyno

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Fadlilatunnisa, Fanny, and Agung Mulyo Widodo. "Implementation of a Deep Learning Algorithm for The Detection of Cataract Disease Severity in Eyes." Infact: International Journal of Computers 9, no. 01 (2025): 35–43. https://doi.org/10.61179/infact.v9i01.712.

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Cataracts cloud the lens of the eye, resulting in blindness and poor vision. Approximately 18 million people are blind due to cataracts, according to the WHO. Prompt diagnosis is essential to avoid problems. This research creates a deep learning model that uses Convolutional Neural Networks (CNN) to categorise cataract severity into four groups: hypermature, normal, immature, and mature. Transfer learning is used with three CNN architectures: VGG16, VGG19, and ResNet50. Experiments from various eras were carried out using a labelled eye picture dataset for training. Using the confusion matrix,

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Arisula, Juan Pala, and Parjito Parjito. "COMPARISON OF NAIVE BAYES AND RANDOM FOREST METHODS IN SENTIMENT ANALYSIS ON THE GETCONTACT APPLICATION." Jurnal Teknik Informatika (Jutif) 5, no. 5 (2024): 1221–30. https://doi.org/10.52436/1.jutif.2024.5.5.2004.

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The rapid growth in the use of social media and instant messaging platform apps has significantly changed the way people communicate. One of the most popular apps is GetContact, a platform focused on identifying the phone numbers of irresponsible people and reducing the impact of spam calls. In cases like this, sentiment analysis is important to understand user responses to the service. In performing sentiment analysis, there are two classification methods that will be used, namely the Naive Bayes and Random Forest methods. This research utilizes the SMOTE technique to handle data imbalance, a

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Susilayasa, I. Made Adi, Anak Agung Istri Eka Karyawati, Luh Gede Astuti, Luh Arida Ayu Rahning Putri, I. Gede Arta Wibawa, and I. Komang Ari Mogi. "Analisis Sentimen Ulasan E-Commerce Pakaian Berdasarkan Kategori dengan Algoritma Convolutional Neural Network." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 11, no. 1 (2022): 1. http://dx.doi.org/10.24843/jlk.2022.v11.i01.p01.

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Almost everyone looks at reviews before deciding to buy an item in e-commerce. Consumers say that online reviews influence their purchasing decisions. Based on these data, consumers need sentiment reviews to make a decision to choose a product/service. However, the results of the sentiment analysis are still less specific, so the review classification process is carried out based on the review category. Sentiment classification process based on clothing category is carried out using the Convolutional neural network method. The amount of data used is 3384 data with 3 categories. The category cl

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Rajalashmi, K., S. Manivannan, and R. Subbulakshmi. "Detecting and Tracking of Road Boundary Lines Using Machine Learning Approach." Indian Journal Of Science And Technology 18, no. 6 (2025): 477–86. https://doi.org/10.17485/ijst/v18i6.3685.

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Objectives: The study aims to improve road safety by developing a lane detection model that achieves high accuracy even in adverse conditions like poor lighting, occlusions, and inclement weather. In high-traffic countries like India, where poor lane discipline contributes to road accidents, this system can significantly enhance driver assistance. Methods: This study compares three approaches—Canny Edge Detection, Hough Transform, and a CNN-based model. The CNN was trained on a dataset of over 1,000 annotated images featuring diverse scenarios, including curves, shadows, and varied lighting co

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A.M., Oyelakin, Alimi O. M, Mustapha I. O, and Ajiboye I. K. "Analysis of Single and Ensemble Machine Learning Classifiers for Phishing Attacks Detection." International Journal of Software Engineering and Computer Systems 7, no. 2 (2021): 44–49. http://dx.doi.org/10.15282/ijsecs.7.2.2021.5.0088.

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Phishing attacks have been used in different ways to harvest the confidential information of unsuspecting internet users. To stem the tide of phishing-based attacks, several machine learning techniques have been proposed in the past. However, fewer studies have considered investigating single and ensemble machine learning-based models for the classification of phishing attacks. This study carried out performance analysis of selected single and ensemble machine learning (ML) classifiers in phishing classification. The focus is to investigate how these algorithms behave in the classification of

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Alsabry, Ayman, and Malek Algabri. "Iterative Tuning of Tree-Ensemble-Based Models' parameters Using Bayesian Optimization for Breast Cancer Prediction." Informatics and Automation 23, no. 1 (2024): 129–68. http://dx.doi.org/10.15622/ia.23.1.5.

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The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used

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Uhryn, Dmytro, Victoria Vysotska, Daryna Zadorozhna, Mariia Spodaryk, Kateryna Hazdiuk, and Zhengbing Hu. "Intelligent Application for Predicting Diabetes Spread Risk in the World Based on Machine Learning." International Journal of Intelligent Systems and Applications 17, no. 3 (2025): 90–144. https://doi.org/10.5815/ijisa.2025.03.06.

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This paper presents the development and implementation of an intelligent system for predicting the risk of diabetes spread using machine learning techniques. The core of the system relies on the analysis of the Pima Indians Diabetes dataset through k-nearest neighbours (k-NN), Random Forest, Logistic Regression, Decision Trees and XGBoost algorithms. After pre-processing the data, including normalization and handling missing values, the k-NN model achieved an accuracy of 77.2%, precision of 80.0%, recall of 85.0%, F1-score of 83.0% and ROC of 81.9%. The Random Forest model achieved an accuracy

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Chiawa, Victor C., and Kingsley Chibueze. "Predictive Modeling of Hepatitis using Machine Learning Algorithms and Mathematical Formulations." International Journal of Development Mathematics (IJDM) 2, no. 2 (2025): 278–97. https://doi.org/10.62054/ijdm/0202.16.

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Hepatitis continues to be a major health concern for the world, mostly impacting those who are most at risk, such as infants and pregnant women. This research aims to develop a machine learning model for early hepatitis detection and reduce the number of people affected and killed by the disease. Characterization of Hepatitis B (HBV), Hepatitis C (HCV), and Hepatitis E (HEV) was done using information from a range of demographics, drawn from healthcare facilities and online repositories. Advanced data preprocessing techniques including feature selection, imputation, and normalization were empl

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Galih Ilham Maulana Putra, Muhammad Sihabudin Riyadi, Adam Maulana, and Siti Maesaroh. "Analysis of the Application of Machine Learning Algorithm in Spam Detection System: Literature Review." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 1615–21. https://doi.org/10.59934/jaiea.v4i3.965.

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Spam detection is an evolving issue in line with the increasing volume of data and the evolution of spam techniques. In recent years, the application of machine learning (ML) algorithms has become an effective solution to enhance the accuracy and efficiency of spam detection systems. This study aims to analyze various machine learning algorithms applied in spam detection systems through a literature review. Several popular algorithms used in spam detection include Naive Bayes, Support Vector Machine (SVM), Neural Network, Recurrent Neural Network (RNN), and Transformer-based models. Each algor

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Li, Xuran, Peng Wu, and Jing Su. "Accurate Fairness: Improving Individual Fairness without Trading Accuracy." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14312–20. http://dx.doi.org/10.1609/aaai.v37i12.26674.

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Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sacrifice the other inevitably with side effects of true bias or false fairness. We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy. Informally, it requires the treatments of an individual and the individual's similar counterparts to conform to a uniform target, i.e., the ground truth of the individual. We prove that accurate fairness also implies typical gr

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Arrayyan, Ahmad Zaki, and Sisdarmanto Adinandra. "Early Detection of Diabetes Mellitus in Women via Machine Learning." Journal of Electrical Technology UMY 8, no. 2 (2025): 44–50. https://doi.org/10.18196/jet.v8i2.24287.

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Diabetes Mellitus (DM) is a major global health concern, responsible for 6.7 million deaths in 2021, equivalent to one death every five seconds. In Indonesia, it was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people. This study focuses on developing a diabetes prediction model using machine learning, aiming for an accuracy of at least 85%, and incorporates a chatbot-based system to identify potential diabetes in women. The research utilizes primary data, including glucose levels, blood pressure, body mass index, and age, as well as second

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Renuka, Oladri, and Niranchana Radhakrishnan. "BERT for Twitter Sentiment Analysis: Achieving High Accuracy and Balanced Performance." Journal of Trends in Computer Science and Smart Technology 6, no. 1 (2024): 37–50. http://dx.doi.org/10.36548/jtcsst.2024.1.003.

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The Bidirectional Encoder Representations from Transformers (BERT) model is used in this work to analyse sentiment on Twitter data. A Kaggle dataset of manually annotated and anonymized COVID-19-related tweets was used to refine the model. Location, tweet date, original tweet content, and sentiment labels are all included in the dataset. When compared to the Multinomial Naive Bayes (MNB) baseline, BERT's performance was assessed, and it achieved an overall accuracy of 87% on the test set. The results indicated that for negative feelings, the accuracy was 0.93, the recall was 0.84, and the F1-s

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Paudyal, Rajendra, and Dhiraj Pyakurel. "Enhancing the Efficiency of Deep Learning Models for Handwritten Text Recognition by Utilizing Meta-learning Optimization Techniques." Journal of Advanced College of Engineering and Management 9 (November 14, 2024): 1–13. http://dx.doi.org/10.3126/jacem.v9i1.71399.

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Recognizing handwritten text plays a crucial role in converting scanned documents, whether printed or handwritten, into editable and searchable formats. In this study, various models such as CRNN, TCN, and Transformer have been utilized for Handwritten Text Recognition (HTR), where input data consists of sequences of image patches representing English text. The CRNN model employed comprises three layers: a CNN for extracting feature maps from handwritten text images, and Bidirectional Long-Short Term Memory (BLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) in the RNN layer to address the

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Lee, Sang-Hyun. "A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis." Applied Sciences 13, no. 20 (2023): 11258. http://dx.doi.org/10.3390/app132011258.

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In this study, we identified the different causes of odor problems and their associated discomfort. We also recognized the significance of public health and environmental concerns. To address odor issues, it is vital to conduct precise analysis and comprehend the root causes. We suggested a hybrid model of a Convolutional Neural Network (CNN) and Transformer called the CNN–Transformer to tackle this challenge and assessed its effectiveness. We utilized a dataset containing 120,000 samples of odor to compare the performance of CNN+LSTM, CNN, LSTM, and ELM models. The experimental results show t

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Maspaeni, Maspaeni, Bahtiar Imran, Alfian Hidayat, and Surni Erniwati. "Implementasi Machine Learning untuk Mendeteksi Penyakit Katarak menggunakan Kombinasi Ekstraksi Fitur dan Neural Network Berdasarkan Citra." JTIM : Jurnal Teknologi Informasi dan Multimedia 7, no. 2 (2025): 232–51. https://doi.org/10.35746/jtim.v7i2.621.

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According to data from the World Health Organization (WHO), more than 1.3 billion people worldwide experience visual impairments, with Cataracts being one of the main causes. Cataracts are an eye condition characterized by clouding of the lens, which can lead to blindness if left untreated. This study aims to accurately detect Cataracts using a combination of feature extraction and neural networks, utilizing digital fundus images. The Dataset used consists of 600 fundus images divided into 80% for training and 20% for testing. The feature extraction process is performed to identify distinctive

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Aula, Nurul, Munirul Ula, and Lidya Rosnita. "ANALISIS SENTIMEN REVIEW CUSTOMER TERHADAP PERUSAHAAN EKSPEDISI JNE, J&T EXPRESS DAN POS INDONESIA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)." JOURNAL OF INFORMATICS AND COMPUTER SCIENCE 9, no. 1 (2023): 81. http://dx.doi.org/10.33143/jics.v9i1.2947.

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Abstrak— Kepuasan customer adalah masalah yang harus diamati pada sebuah perusahaan, karena customer adalah alasan mengapa suatu perusahaan masih berdiri dan sukses. Perusahaan ekspedisi JNE, J&T, dan Pos Indonesia mempunyai akun twitter layanan customer yaitu @Jnecare, @J&texpressid dan @Posindonesia. Akun ini digunakan untuk layanan customer secara online yang disediakan untuk menyampaikan pendapat, kritik, saran atau keluhan pelanggan. Agar dapat mengolah komentar yang banyak tentu membutuhkan waktu yang lebih besar jika hanya dilakukan secara sederhana. Penelitian ini bertujuan unt

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Korkmaz, Aras Fahrettin, Fatih Ekinci, Şehmus Altaş, Eda Kumru, Mehmet Serdar Güzel, and Ilgaz Akata. "A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species." Biology 14, no. 6 (2025): 719. https://doi.org/10.3390/biology14060719.

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This study presents a novel approach for classifying Discomycetes species using deep learning and explainable artificial intelligence (XAI) techniques. The EfficientNet-B0 model achieved the highest performance, reaching 97% accuracy, a 97% F1-score, and a 99% AUC, making it the most effective model. MobileNetV3-L followed closely, with 96% accuracy, a 96% F1-score, and a 99% AUC, while ShuffleNet also showed strong results, reaching 95% accuracy and a 95% F1-score. In contrast, the EfficientNet-B4 model exhibited lower performance, achieving 89% accuracy, an 89% F1-score, and a 93% AUC. These

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Indrajaya, Denny, Adi Setiawan, and Bambang Susanto. "Comparison of k-Nearest Neighbor and Naive Bayes Methods for SNP Data Classification." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22, no. 1 (2022): 149–64. http://dx.doi.org/10.30812/matrik.v22i1.1758.

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In an accident, sometimes the identity of a person who has an accident is hard to know, so it is necessary to use biological data such as Single Nucleotide Polymorphism (SNP) data to identify the person's origin. This research aims to compare the accuracy and the F1 score of the k-Nearest Neighbor method and the Naive Bayes method in classifying SNP data from 120 people who divide into groups, namely European (CEU) and Yoruba (YRI). Determination of the best method based on the average value of accuracy and the average value of F1 score from 1000 iterations with various percentage distribution

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Kurniastuti, Ima, Teguh Herlambang, Tri Deviasari Wulan, et al. "Improved Edge Detection using Morphological Operation to Segmentation of Fingernail Images." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 1 (2025): 165–75. https://doi.org/10.35882/jeeemi.v7i1.589.

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Accurate segmentation of fingernail images is essential for biomedical applications like dermatological diagnostics and nail disease assessments. This study compares traditional methods (Sobel and Canny edge detectors) with an improved method using adaptive thresholding and morphological closing for fingernail image segmentation. The methodology includes data collection, preprocessing, edge detection, segmentation, and evaluation. A dataset of 500 fingernail images (free of nail polish) was captured using a digital camera. Preprocessing involves grayscale conversion to simplify analysis and Ga

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K, Rajalashmi, Manivannan S, and Subbulakshmi R. "Detecting and Tracking of Road Boundary Lines Using Machine Learning Approach." Indian Journal of Science and Technology 18, no. 6 (2025): 477–86. https://doi.org/10.17485/IJST/v18i6.3685.

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<strong>Objectives:</strong> The study aims to improve road safety by developing a lane detection model that achieves high accuracy even in adverse conditions like poor lighting, occlusions, and inclement weather. In high-traffic countries like India, where poor lane discipline contributes to road accidents, this system can significantly enhance driver assistance.<strong> Methods:</strong> This study compares three approaches—Canny Edge Detection, Hough Transform, and a CNN-based model. The CNN was trained on a dataset of over 1,000 annotated images featuring diverse scena

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Shi, Yuanwu, and Jiuye Sun. "Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population." PeerJ Computer Science 10 (October 31, 2024): e2436. http://dx.doi.org/10.7717/peerj-cs.2436.

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As the population ages, the increase in the number of middle-aged and older adults with diabetes poses new challenges to the allocation of resources in the healthcare system. Developing accurate diabetes prediction models is a critical public health strategy to improve the efficient use of healthcare resources and ensure timely and effective treatment. In order to improve the identification of diabetes in middle-aged and older patients, a Bagging-RF model is proposed. In the study, two diabetes datasets on Kaggle were first preprocessed, including unique heat coding, outlier removal, and age s

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Aufa Hawari, Fadli, and Ira Diana Sholihati. "PERBANDINGAN METODE NAÏVE BAYES DAN K-NEAREST NEIGHBORS DALAM KLASIFIKASI KEPUASAN MAHASISWA TERHADAP LAYANAN WI-FI DI UNIVERSITAS NASIONAL." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 3 (2025): 5203–8. https://doi.org/10.36040/jati.v9i3.13554.

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Layanan Wi-Fi di perguruan tinggi sangat penting bagi mahasiswa untuk menunjang kegiatan akademiknya. Namun, kualitas layanan Wi-Fi di Universitas Nasional masih menjadi perhatian utama karena berbagai keluhan pengguna. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Naïve Bayes dan K-Nearest Neighbor (KNN) dalam mengklasifikasi tingkat kepuasan mahasiswa terhadap layanan Wi-Fi. Data dikumpulkan melalui survei berbasis Google Forms dan dianalisis menggunakan Google Colab. Evaluasi kinerja dilakukan berdasarkan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian me

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Bahri, Samsul, Ema Utami, and Asro Nasiri. "Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4.5 Algorithm." CCIT Journal 15, no. 2 (2022): 198–207. http://dx.doi.org/10.33050/ccit.v15i2.2286.

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To improve public services, accuracy and acceleration are needed in classifying the types of complaints so that complaints can immediately get a response from the relevant regional apparatus. This public complaint data is in text form and is not balanced in each category of regional apparatus, so we contribute to research to compare the performance of different text mining-based classification algorithms. In addition, we also tested the resampling method to overcome imbalanced data. In the final stage, testing is carried out using a multiclass confusion matrix table to show accuracy, precision

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Sanjeet Kumar, Jameel Ahmad. "Emotion Classification from Covid-19Pandemic Tweets using RoBERTa." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 546–54. https://doi.org/10.52783/jisem.v10i32s.5336.

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This study proposes a hybrid sentiment analysis model combining RoBERTa (Robustly Optimized BERT Pretraining Approach), a state-of-the-art pre-trained transformer, with SVM (Support Vector Machine) for enhanced performance in predicting sentiments. The model's performance is evaluated across several key metrics, including accuracy, precision, recall, and F1 score, and it outperforms other models such as "SVM," "RoBERTa," and "BERT-BiLSTM (Bidirectional Encoder Representations from Transformers) and BiLSTM (Bidirectional Long Short-Term Memory) ". The proposed RoBERTa-SVM model achieves the hig

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Zhu, Tong, Fengyi Yan, Xinyang Lv, et al. "A Deep Learning Model for Accurate Maize Disease Detection Based on State-Space Attention and Feature Fusion." Plants 13, no. 22 (2024): 3151. http://dx.doi.org/10.3390/plants13223151.

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In improving agricultural yields and ensuring food security, precise detection of maize leaf diseases is of great importance. Traditional disease detection methods show limited performance in complex environments, making it challenging to meet the demands for precise detection in modern agriculture. This paper proposes a maize leaf disease detection model based on a state-space attention mechanism, aiming to effectively utilize the spatiotemporal characteristics of maize leaf diseases to achieve efficient and accurate detection. The model introduces a state-space attention mechanism combined w

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Hor Yan, Tan, Sazuan Nazrah Mohd Azam, Zamani Md. Sani, and Azizul Azizan. "Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 366. http://dx.doi.org/10.11591/ijece.v14i1.pp366-374.

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This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18

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Sri, Hartini, and Rustam Zuherman. "The comparison study of kernel KC-means and support vector machines for classifying schizophrenia." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 3 (2020): 1643–49. https://doi.org/10.12928/TELKOMNIKA.v18i3.14847.

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Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the

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