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Journal articles on the topic 'Kaggle Dataset'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Lo, Jui-En, Eugene Yu-Chuan Kang, Yun-Nung Chen, et al. "Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy." Journal of Diabetes Research 2021 (December 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/2751695.

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This study is aimed at evaluating a deep transfer learning-based model for identifying diabetic retinopathy (DR) that was trained using a dataset with high variability and predominant type 2 diabetes (T2D) and comparing model performance with that in patients with type 1 diabetes (T1D). The Kaggle dataset, which is a publicly available dataset, was divided into training and testing Kaggle datasets. In the comparison dataset, we collected retinal fundus images of T1D patients at Chang Gung Memorial Hospital in Taiwan from 2013 to 2020, and the images were divided into training and testing T1D d

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Jesie, R. Sherline, and M. S. Godwin Premi. "Improved Tunicate Swarm Optimization Based Hybrid Convolutional Neural Network for Classification of Leaf Diseases and Nutrient Deficiencies in Rice (Oryza)." Agronomy 14, no. 8 (2024): 1851. http://dx.doi.org/10.3390/agronomy14081851.

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In Asia, rice is the most consumed grain by humans, serving as a staple food in India. The yield of rice paddies is easily affected by nutrient deficiencies and leaf diseases. To overcome this problem and improve the yield productivity of rice, nutrient deficiency and leaf disease identification are essential. The main nutrient elements in paddies are potassium, phosphorus, and nitrogen (PPN), the deficiency of any of which strongly affects the rice plants. When multiple nutrient elements are deficient, the leaf color of the rice plants is altered. To overcome this problem, optimal nutrient de

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Tsai, Chi-Yi, Wei-Hsuan Shih, and Humaira Nisar. "Three-Stage Recursive Learning Technique for Face Mask Detection on Imbalanced Datasets." Mathematics 12, no. 19 (2024): 3104. http://dx.doi.org/10.3390/math12193104.

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In response to the COVID-19 pandemic, governments worldwide have implemented mandatory face mask regulations in crowded public spaces, making the development of automatic face mask detection systems critical. To achieve robust face mask detection performance, a high-quality and comprehensive face mask dataset is required. However, due to the difficulty in obtaining face samples with masks in the real-world, public face mask datasets are often imbalanced, leading to the data imbalance problem in model training and negatively impacting detection performance. To address this problem, this paper p

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Nafi'iyah, Nur, and Nur Fahmi Maulidi. "LINEAR REGRESSION FOR DISCOUNTING PRESENTATION RECOMMENDATIONS (Kaggle Dataset)." JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI 13, no. 2 (2022): 67–73. http://dx.doi.org/10.51903/jtikp.v13i2.326.

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In the business of selling goods, there must be goods that do not sell well and sell well. How to make unsold items sell well by giving customers a discount or discount strategy. The goal is to provide discounted prices to attract customers' attention and increase sales turnover. Prediction to give the right discount presentation is needed in the discount strategy. How to determine discount prediction using linear regression method, looking for line equations by training data taken from Kaggle.com. The data were trained to find the constants and coefficients of the independent variables. The r

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Jinfeng, Gao, Sehrish Qummar, Zhang Junming, Yao Ruxian, and Fiaz Gul Khan. "Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection." Computational Intelligence and Neuroscience 2020 (December 15, 2020): 1–11. http://dx.doi.org/10.1155/2020/8864698.

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Diabetic retinopathy (DR) is an eye disease that damages the blood vessels of the eye. DR causes blurred vision or it may lead to blindness if it is not detected in early stages. DR has five stages, i.e., 0 normal, 1 mild, 2 moderate, 3 severe, and 4 PDR. Conventionally, many hand-on projects of computer vision have been applied to detect DR but cannot code the intricate underlying features. Therefore, they result in poor classification of DR stages, particularly for early stages. In this research, two deep CNN models were proposed with an ensemble technique to detect all the stages of DR by u

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Anaya-Sánchez, Héctor, Leopoldo Altamirano-Robles, Raquel Díaz-Hernández, and Saúl Zapotecas-Martínez. "WGAN-GP for Synthetic Retinal Image Generation: Enhancing Sensor-Based Medical Imaging for Classification Models." Sensors 25, no. 1 (2024): 167. https://doi.org/10.3390/s25010167.

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Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features. We evaluated the method across multiple retinal image datasets

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Sudriyanto, Sudriyanto, Muhammad Ali Hafid, and Moch Ade Kurniawan. "Deteksi Akun Kaggle Bot Menggunakan Linear Regression." Journal of Electrical Engineering and Computer (JEECOM) 6, no. 2 (2024): 449–59. http://dx.doi.org/10.33650/jeecom.v6i2.9251.

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Penelitian ini mengkaji permasalahan pemalsuan akun pada platform Kaggle dengan fokus pada pengembangan model prediksi menggunakan metode Linear Regression untuk mendeteksi akun bot. Kaggle, sebagai platform terkemuka dalam bidang ilmu data, menghadapi tantangan serius terkait integritas data akibat praktik bot voting yang berdampak pada keaslian kompetisi dan dataset yang diunggah. Studi ini memanfaatkan dataset Kaggle Bot Account yang terdiri dari lebih dari satu juta entri, dengan variabel independen mencakup jumlah pengikut, interaksi dengan konten, dan aktivitas pengguna lainnya. Metode L

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van Otterloo, Sieuwert, and Pavlo Burda. "The Utrecht Housing dataset: A housing appraisal dataset." Computers and Society Research Journal 1 (2025): 1–11. https://doi.org/10.54822/qvhm1662.

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This paper introduces a real-world dataset for analysing and predicting house prices. The dataset consists of actual data on the Dutch housing market collected in 2024 for a total of 153 houses in one city (Utrecht in The Netherlands). The dataset incorporates diverse variables on individual houses, includ- ing property characteristics (e.g., house type, build year, geolocation, area, energy label) and market metrics (e.g., asking price, final price). The data was collected from two public sources. The dataset has been created to help researchers and educators to demonstrate machine learning p

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Ahamad, Ghulab Nabi, Shafiullah, Hira Fatima, et al. "Influence of Optimal Hyperparameters on the Performance of Machine Learning Algorithms for Predicting Heart Disease." Processes 11, no. 3 (2023): 734. http://dx.doi.org/10.3390/pr11030734.

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One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparamet

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Aziz, Faisal, and Nana Suryana. "Implementation of Machine Learning for Disease Detection in Tomato Plants Using Convolutional Neural Networks." JESII: Journal of Elektronik Sistem InformasI 2, no. 2 (2024): 275–87. https://doi.org/10.31848/jesii.v2i2.3580.

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Diseases in tomato plants can be highly detrimental to tomato farmers, with common afflictions such as begomovirus, blight, and spider mites posing significant challenges. The implementation of machine learning offers a promising solution to address these issues and mitigate the financial losses caused by such diseases. This study aims to evaluate the effectiveness of machine learning in detecting plant diseases using Convolutional Neural Networks (CNN). The data used in this implementation was obtained from public datasets available on Kaggle and real-time data collected directly from tomato

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Sebakara, Emmanuel, and Dr K. N. Jonathan. "Encrypted Remote Access Trojan Detection: A Machine Learning Approach with Real-World and Open Datasets." Journal of Information and Technology 5, no. 3 (2025): 30–42. https://doi.org/10.70619/vol5iss3pp30-42.

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The increasing use of encryption by cyber attackers to conceal Remote Access Trojans (RATs) challenges traditional signature-based detection systems, which struggle with encrypted traffic and leave security gaps. In this study, we propose a privacy-preserving, machine-learning-based framework that detects encrypted RATs without decrypting traffic. Instead, it analyzes behavioral indicators and metadata, including packet timing anomalies, TLS handshake irregularities, and persistent unidirectional flows. We evaluated our approach using two datasets: a public Kaggle dataset (177,482 labeled reco

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Vituškins, Gļebs, and Sergejs Kodors. "TRAINING OF A CONVOLUTIONAL NEURAL NETWORK FOR HAND GESTURE RECOGNIZING ON THE KAGGLE ASL ALPHABET DATASET." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 27 (October 30, 2023): 28–31. http://dx.doi.org/10.17770/het2023.27.7377.

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Nowadays, hand gesture recognizing is important topic. It is used in virtual assistant work, for sign language translation, in virtual and augmented reality applications, and in entertainment services. The paper deals with the convolutional neural network training using different technologies. The neural network is trained to classify American manual alphabet and 3 extended gestures using photographs. The open access dataset Kaggle ASL Alphabet was used for training. Kaggle ASL Alphabet provides 87000 images of 29 classes for image classification and hand gesture recognizing.

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Wulandari, Serin, Yogi Isro’ Mukti, and Tri Susanti. "Optimization of the Artificial Neural Network Algorithm with Genetic Algorithm in Stroke Prediction." sinkron 8, no. 2 (2024): 1056–63. http://dx.doi.org/10.33395/sinkron.v8i2.13609.

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This study aims to optimize Artificial Neural Network with Genetic Algorithm in predicting stroke. This research is motivated by health problems in the community that are less considered that cause a disease such as stroke. Factors of lifestyle, poor diet and other factors that can be the cause of stroke. Therefore, where later the data that has been obtained will be processed to see what factors determine the cause of stroke. The data used, namely kaggle and mendeley, will be processed using RapidMiner, with a development method (CRISP-DM) and a testing method using a Confusion Matrix. The re

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Hamim, Sultanul Arifeen, Mubasshar U. I. Tamim, M. F. Mridha, Mejdl Safran, and Dunren Che. "SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare." Diagnostics 15, no. 1 (2024): 64. https://doi.org/10.3390/diagnostics15010064.

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Background: Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption. Methods: The SmartSkin-XAI methodology incorporates a fine-tuned DenseNet121 model combined with XAI techniques to interpret predictions. This approach improves early detection and patient management by offe

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Ereshchenko, A. V. "Applying machine learning for solubility prediction: comparing different representations of molecular data." Modelling and Data Analysis 15, no. 1 (2025): 35–50. https://doi.org/10.17759/mda.2025150103.

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<p>Solubility is one of the crucial properties of drugs and is important to determine early in the drug development cycle. Artificial intelligence (AI) based algorithms offer a faster solution compared to much more computationally expensive methods that utilize energy calculations, quantum dynamics and calculation of molecular dynamics. In this work, several AI-based algorithms utilizing different molecular data representation approaches, namely convolutional neural networks, graph neural networks and decision tree based gradient boosting, are applied to an open dataset from a recently p

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Yuichi, Michael, and Yeremia Alfa Susetyo. "Klasifikasi Penyakit Migrain dengan Metode Naïve Bayes pada Dataset Kaggle." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 6, no. 1 (2025): 139–51. https://doi.org/10.35870/jimik.v6i1.1150.

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Migraine is one of the most common neurological diseases in society and has a significant disability impact. According to the Global Burden of Disease Study, migraine is one of the most common neurological disorders in the world, with a greater disability burden than other neurological disorders. In data science, data classification plays an important role in determining the category or class of an object based on a number of available classes. One frequently used classification method is Naïve Bayes, which utilizes mathematical probabilities with the assumption that the decision made is accur

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Khatri, Bhavay. "Detection of Thyroid using Different Machine Learning Approach." International Journal of Research in Science and Technology 12, no. 04 (2022): 11–13. http://dx.doi.org/10.37648/ijrst.v12i04.003.

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The purpose of this paper is to identify the diagnosis of thyroid disease and to classify the two possible types of thyroid disease (hypothyroidism and hyperthyroidism). Numerous machine-learning algorithms are currently being used to identify thyroid disease. Nonetheless, our goal is to implement the machine learning algorithm, which will allow for faster and more accurate diagnosis of thyroid disease and type. The project is being implemented in Python, and the platform from which the dataset was derived is Kaggle. The Kaggle dataset was trained using a variety of machine-learning techniques

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Maçin, Gulay, Fatih Genç, Burak Taşcı, Sengul Dogan, and Turker Tuncer. "KidneyNeXt: A Lightweight Convolutional Neural Network for Multi-Class Renal Tumor Classification in Computed Tomography Imaging." Journal of Clinical Medicine 14, no. 14 (2025): 4929. https://doi.org/10.3390/jcm14144929.

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Background: Renal tumors, encompassing benign, malignant, and normal variants, represent a significant diagnostic challenge in radiology due to their overlapping visual characteristics on computed tomography (CT) scans. Manual interpretation is time consuming and susceptible to inter-observer variability, emphasizing the need for automated, reliable classification systems to support early and accurate diagnosis. Method and Materials: We propose KidneyNeXt, a custom convolutional neural network (CNN) architecture designed for the multi-class classification of renal tumors using CT imaging. The

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Mortezapour Shiri, Farhad, Shingo Yamaguchi, and Mohd Anuaruddin Bin Ahmadon. "A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition." Applied Sciences 15, no. 6 (2025): 2984. https://doi.org/10.3390/app15062984.

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Employee attrition, which causes a significant loss for an organization, is the term used to describe the natural decline in the number of employees in an organization as a result of numerous unavoidable events. If a company can predict the likelihood of an employee leaving, it can take proactive steps to address the issue. In this study, we introduce a deep learning framework based on a Bidirectional Temporal Convolutional Network (Bi-TCN) to predict employee attrition. We conduct extensive experiments on two publicly available datasets, including IBM and Kaggle, comparing our model’s perform

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Phorah, Kokisa, Malusi Sibiya, and Mbuyu Sumbwanyambe. "Prompt Design through ChatGPT’s Zero-Shot Learning Prompts: A Case of Cost-Sensitive Learning on a Water Potability Dataset." Informatics 11, no. 2 (2024): 27. http://dx.doi.org/10.3390/informatics11020027.

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Datasets used in AI applications for human health require careful selection. In healthcare, machine learning (ML) models are fine-tuned to reduce errors, and our study focuses on minimizing errors by generating code snippets for cost-sensitive learning using water potability datasets. Water potability ensures safe drinking water through various scientific methods, with our approach using ML algorithms for prediction. We preprocess data with ChatGPT-generated code snippets and aim to demonstrate how zero-shot learning prompts in ChatGPT can produce reliable code snippets that cater to cost-sens

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Duraichi, N., S. Jalaja, C. D. Merlin, J. S. Meena, R. N. Kamali, and K. Manoj. "Detection and Classification of Diabetic Retinopathy using Deep Learning." CARDIOMETRY, no. 26 (March 1, 2023): 808–13. http://dx.doi.org/10.18137/cardiometry.2023.26.808813.

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Detection of Diabetic Retinopathy at the early stages could significantly reduce the need for complicated and expensive surgeries. The availability of large datasets has fuelled research in this field. In this project, diabetic retinopathy is detected and classified into five stages: no DR, severe DR, Moderate DR, Proliferative DR, and mild DR. This is made possible with the help of various deep learning techniques. A trained model (ResNet-50 architecture) is used for the extraction of various features from the images. This model gives an accuracy of 0.47% in testing. The dataset used is the A

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Bali, Manik. "Signature Verification System Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30155.

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The proposed system employs a convolutional neural network (CNN) architecture for signature feature extraction and classification. Furthermore, the system integrates preprocessing modules for signature image normalization, noise reduction, and feature extraction to enhance the robustness and accuracy of the verification process. Extensive experimentation and evaluation are conducted on benchmark datasets, including the widely used Tobacco 800 dataset and Kaggle dataset, to assess the performance of the proposed system in terms of accuracy, precision, recall, and score metrics. The results demo

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Bhimavarapu, Usharani, and Gopi Battineni. "Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function." Healthcare 11, no. 1 (2022): 97. http://dx.doi.org/10.3390/healthcare11010097.

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Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely used in DR detection through the classification of blood vessel pixels from the remaining pixels. In this paper, an improved activation function was proposed for diagnosing DR from fundus images that automatically reduces loss and processing time. The DIARETDB0, DRIVE, CHASE, and Kaggle datasets were used to trai

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Akoosh, Lamiaa Mohammed Salem, Farheen Siddiqui, Sherin Zafar, Sameena Naaz, and M. Afshar Alam. "An experiment-based investigation into machine learning for predicting coronary heart disease." Journal of Statistics and Management Systems 27, no. 2 (2024): 441–53. http://dx.doi.org/10.47974/jsms-1278.

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Extensive inquiry has been conducted to explore potential applications of machine learning methodologies in the realm of cardiovascular disease management. To facilitate a more comprehensive investigation This study explores machine learning algorithms, specifically Support Vector Machines (SVM) and Artificial Neural Networks (ANN), for disease identification, focusing on cardiovascular diseases. Utilizing a Kaggle dataset of around seventy thousand medical records, the research aims to refine methodology and assess performance variations. SVM and ANN techniques are applied to the Kaggle datas

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Mohammad Amir Khan, Ahmed Rimaz Faizabadi, Muhammad Mahabubur Rashid, and Hasan Firdous Zaki. "Performance Evaluation of State-of-The-Art 2D Face Recognition Algorithms on Real and Synthetic Masked Face Datasets." Journal of Advanced Research in Applied Sciences and Engineering Technology 30, no. 2 (2023): 225–42. http://dx.doi.org/10.37934/araset.30.2.225242.

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Face recognition systems based on Convolutional neural networks have recorded unprecedented performance for multiple benchmark face datasets. Due to the Covid-19 outbreak, people are now compelled to wear face masks to reduce the virus's transmissibility. Recent research shows that when given the masked face recognition scenario, which imposes up to 70% occlusion of the face area, the performance of the FR algorithms degrades by a significant margin. This paper presents an experimental evaluation of a subset of the MFD-Kaggle and Masked-LFW (MLFW) datasets to explore the effects of face mask o

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Vaman M. Haji. "Enhanced SVM Classification for Diabetes Prediction: A Comparative Analysis Using the Kaggles Diabetes Dataset." Communications on Applied Nonlinear Analysis 31, no. 8s (2024): 237–47. http://dx.doi.org/10.52783/cana.v31.1477.

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Diabetes mellitus is a significant global health concern that impacts a large number of individuals globally and imposes a substantial financial burden on healthcare systems. The aim of this study is to use machine learning methods, namely Support Vector Machines (SVM), to develop a prediction model for assessing the risk of diabetes using the Kaggle diabetes dataset. We used a comprehensive dataset sourced from Kaggle, which encompasses several health metrics such as age, body mass index (BMI), glucose levels, and other relevant factors. In order to identify patterns that indicate a potential

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Jin, Jing, and Yongqing Zhang. "Innovation in Financial Enterprise Risk Prediction Model." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–26. http://dx.doi.org/10.4018/joeuc.361650.

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In the context of predicting financial risks for enterprises, traditional methods are inadequate in capturing complex multidimensional data features, resulting in suboptimal prediction performance. Although existing deep learning techniques have shown some improvements, they still face challenges in processing time series data and detecting extended dependencies. To address these issues, this paper proposes an integrated deep learning framework utilizing Convolutional Neural Network (CNN), Transformer model, and Wavelet Transform (WT). The proposed model leverages CNN to derive local features

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Feng, Ziyuan, Zixian Liu, and Yibo Yin. "Comparison of deep-learning and conventional machine learning algorithms for salary prediction." Applied and Computational Engineering 6, no. 1 (2023): 757–65. http://dx.doi.org/10.54254/2755-2721/6/20230910.

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Salary is an integral part of contemporary life. With the large-scale use of machine learning, it has become possible to predict salaries with machine learning. Previous researchers have used random forest algorithms to solve this problem, however, there is a research gap in using a neural network to solve this problem. Therefore, the research topic of this paper is to use convolutional neural networks (CNN) and datasets on Kaggle to predict salary. The research methodology of this paper is as follows. First, the Kaggle dataset is divided into the train-dataset and the test-dataset. After prep

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D, Cenitta, VIJAYA ARJUNAN RANGANATHAN, Tanuja Shailesh, Andrew J, Arul N, and Praveen Pai T. "Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction." F1000Research 14 (July 3, 2025): 650. https://doi.org/10.12688/f1000research.165575.1.

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Background Early and accurate prediction of ischemic heart disease (IHD) is essential for reducing mortality and enabling timely intervention. Misdiagnosis can lead to severe health outcomes, emphasizing the need for robust and intelligent predictive models. Deep learning approaches have shown strong potential in identifying hidden patterns in medical data and aiding clinical decision-making. Methods This study proposes a novel Hybrid Residual Attention with Echo State Network (HRAESN) model that integrates Attention Residual Learning (ARL) with Echo State Networks (ESN) to enhance feature ext

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Tehsin, Sara, Inzamam Mashood Nasir, Robertas Damaševičius, and Rytis Maskeliūnas. "DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection." Big Data and Cognitive Computing 8, no. 9 (2024): 97. http://dx.doi.org/10.3390/bdcc8090097.

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Brain tumors are the result of irregular development of cells. It is a major cause of adult demise worldwide. Several deaths can be avoided with early brain tumor detection. Magnetic resonance imaging (MRI) for earlier brain tumor diagnosis may improve the chance of survival for patients. The most common method of diagnosing brain tumors is MRI. The improved visibility of malignancies in MRI makes therapy easier. The diagnosis and treatment of brain cancers depend on their identification and treatment. Numerous deep learning models are proposed over the last decade including Alexnet, VGG, Ince

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Arif, Zhafeni, R. Yunendah Nur Fu’adah, Syamsul Rizal, and Divo Ilhamdi. "Classification of eye diseases in fundus images using Convolutional Neural Network (CNN) method with EfficientNet architecture." JRTI (Jurnal Riset Tindakan Indonesia) 8, no. 1 (2023): 125. http://dx.doi.org/10.29210/30032835000.

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This research is designed to classify eye disease conditions into three classes, namely normal, cataract, and glaucoma using a system. The system will use Convolutional Neural Network (CNN) with EfficientNet architecture. The EfficientNet that will be used is EfficientNet-B0. The dataset in this paper is obtained from Kaggle totaling 300 images. From this data, augmentation is carried out so that 3.600 images are obtained consisting of "normal" (1200 images), "cataract" (1200 images), and "glaucoma" (1200 images). This data is processed into 4 different datasets, namely the original dataset, a

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Paul Anule, Asoshi, and Chukwudi Jennifer Ifeoma. "Application of Support Vector Machine for Effective Prediction of Election for Sentiment Analysis." Middle East Journal of Applied Science & Technology 08, no. 01 (2025): 115–31. https://doi.org/10.46431/mejast.2025.8111.

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This study proposes the use of machine learning models, namely Support Vector Machine (SVM), for effective sentiment analysis on a dataset from the Kaggle repository. Considering the Tinubu 2023 election dataset, it can be seen that SVM having been fed with the cleansed dataset feature obtained an accuracy score of 93.2%, considering the result of each algorithm on the 2023 Nigerian election datasets. The study investigates data preprocessing techniques, feature selection, and correlation metrics to optimize the sentiment detection process. Results show that the SVM model achieves the highest

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Almufareh, Maram Fahaad, Samabia Tehsin, Mamoona Humayun, and Sumaira Kausar. "Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease." Healthcare 11, no. 20 (2023): 2763. http://dx.doi.org/10.3390/healthcare11202763.

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Alzheimer’s disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer’s in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer’s disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs

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Nia, Zahra Movahedi, Ali Ahmadi, Bruce Mellado, et al. "Twitter-based gender recognition using transformers." Mathematical Biosciences and Engineering 20, no. 9 (2023): 15957–77. http://dx.doi.org/10.3934/mbe.2023711.

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<abstract> <p>Social media contains useful information about people and society that could help advance research in many different areas of health (e.g. by applying opinion mining, emotion/sentiment analysis and statistical analysis) such as mental health, health surveillance, socio-economic inequality and gender vulnerability. User demographics provide rich information that could help study the subject further. However, user demographics such as gender are considered private and are not freely available. In this study, we propose a model based on transformers to predict the user's

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Sirohi, Devansh, Deepanshu Kashyap, Devendra Pal, Gopal Goyal, and Bhumica Verma. "Thyroid Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 241–45. http://dx.doi.org/10.22214/ijraset.2023.48531.

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Abstract: This paper aims to recognize the diagnosis of the thyroid disease and then categorize the type of thyroid disease a patient may be suffering from (i.e., hyperthyroidism or hypothyroidism). The project implementation is being done by using python and Kaggle is the platform from which the dataset has been taken. At present many machine learning algorithms have been used to detect thyroid disease like but our goal is to implement the machine learning algorithm which has higher accuracy and which takes less time in detecting the disease along with the type of thyroid. We have trained the

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Yasser Maatouk, Yasser Maatouk. "From Bibliometrics to Altmetrics: Examining the Relationship Between Citation Count and Altmetric Score in Publications on Artificial Intelligence." journal of King Abdulaziz University Computing and Information Technology Sciences 11, no. 2 (2022): 45–52. http://dx.doi.org/10.4197/comp.11-2.4.

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the Convolution Neural Network (CNN) is the most widely used deep learning architecture as it has broken most world records for recognition tasks. Facial Expression Recognition (FER) systems that use classical feature- based techniques, especially CNN’s, is best for classifying images. This paper used three CNN-based methods, which are VGG-16, Inception-v3, and Resnet50-V2 network architectures, to classify facial expressions into seven classes of emotions: happy, angry, neutral, sad, disgust, fear, and surprise. The face expression dataset from Kaggle and JAFFE dataset were used to compare th

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Noreen, Iram, Muhammad Hamid, Uzma Akram, Saadia Malik, and Muhammad Saleem. "Hand Pose Recognition Using Parallel Multi Stream CNN." Sensors 21, no. 24 (2021): 8469. http://dx.doi.org/10.3390/s21248469.

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Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to be

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Dalal, Surjeet, Pallavi Goel, Edeh Michael Onyema, et al. "Application of Machine Learning for Cardiovascular Disease Risk Prediction." Computational Intelligence and Neuroscience 2023 (March 1, 2023): 1–12. http://dx.doi.org/10.1155/2023/9418666.

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Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neur

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Rahayu, Mina Ismu, Faiqunisa, and Nugraha. "Cat Breed Classification Using Kaggle Dataset Metadata With YOLO v5 Framework." Jurnal Teknologi Informasi dan Komunikasi 12, no. 1 (2023): 14–18. http://dx.doi.org/10.58761/jurtikstmikbandung.v12i1.1418.

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Pada saat ini kucing memiki berbagai macam ras yang berbeda-beda di seluruh dunia diantaranya ada Persian, Maine Coon, Siamese, Ragdoll, Sphynx dan lain-lain. Untuk mengetahuinya setiap ras kucing bisa dilihat dari pola, warna bulu, dan ada beberapa wajahnya yang berbeda dengan kucing-kucing lainnya, namun tidak dapat sepenuhnya pola, warna bulu dan wajah dapat membedakan setiap ras kucing. Dengan berkembangnya zaman dan meningkatnya teknologi di bidang Computer Vision dimana sistem Artificial Intelligence yang dilatih dimanfaatkan sebagai alat untuk mengklasifikasikan jenis ras kucing menggun

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Betgeri, Prof Santusti. "Mammary Tumor Screening." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5962–67. https://doi.org/10.22214/ijraset.2025.71017.

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Mammary Tumor Screening using deep learning provides an innovative approach for early breast cancer detection. In this work, a model trained on Convolutional Neural Networks (CNNs) on the Kaggle Multi Cancer dataset, consisting of 10,000 high-resolution histopathological images of benign and malignant tumors. To improve model performance and lessen overfitting, preprocessing methods like resizing, normalisation, and data augmentation are used. The CNN model .The CNN model is designed for binary classification, and itsF1-score, recall, accuracy, and precision are used to assess performance. Thi

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P.UmaMaheswari, A.Kumar Kombaiya. "Enhancing Social Media Influence with Cutting-Edge Machine Learning Approaches." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 910–24. https://doi.org/10.52783/cana.v32.4039.

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Homophily and community influence play the important roles in shaping user behavior towards others and towards content in social recommendation systems. This aspect has a considerable effect on how individuals engage with materials. Homophily is a core concept in social network analysis: it is the tendency of individuals to associate with others who have similar interests or characteristics. This research begins with datasets collected from Facebook, Instagram, and YouTube. Preprocessing occurred after the dataset was obtained from Kaggle sources. Following the completion of the initial datase

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Adeyanju, I. A., O. O. Bello, and M. A. Azeez. "Development of an American Sign Language Recognition System using Canny Edge and Histogram of Oriented Gradient." Nigerian Journal of Technological Development 19, no. 3 (2022): 195–205. http://dx.doi.org/10.4314/njtd.v19i3.2.

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Sign language is used by people who have hearing and speaking difficulties, but not understood by many without these difficulties. Therefore, sign language recognition systems are developed to aid communication between hearing impaired people and others. This paper developed a static American Sign Language Recognition (ASLR) system using canny-edge and histogram of oriented gradient (HOG) for feature extraction with K-Nearest Neighbour (K-NN) as classifier. The sign language image datasets used consist of English alphabets from both Massey University and Kaggle, and numbers (0-9) from Massey U

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Pratama, Yogi Tiara, Sukemi Sukemi, and Bambang Tutuko. "Utilizing IoT-Enhanced Multilayer Perceptron and Run Length Encoding for Classifying Plant Suitability Based on pH and Soil Humidity Parameters." Journal of Information Systems and Informatics 6, no. 3 (2024): 2022–36. http://dx.doi.org/10.51519/journalisi.v6i3.811.

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This research proposes an IoT-based system for classifying plant suitability using pH data and soil humidity parameters. The system utilizes Run-Length Encoding (RLE) to compress sensor data, which is transmitted to a database server via the Esp8266 module. A Multilayer Perceptron (MLP) algorithm is employed to classify the data, achieving an accuracy of 0.82 with only two parameters. The classification results are displayed on a website, providing real-time recommendations for farmers. The system's performance is evaluated using a dataset from Kaggle. The Kaggle dataset contains 2200 instance

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Assegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased t

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Tsehay, Admassu Assegie, Lakshmi Tulasi R., and Komal Kumar N. "Breast cancer prediction model with decision tree and adaptive boosting." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 184–90. https://doi.org/10.11591/ijai.v10.i1.pp184-190.

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased t

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El-Latif, Ahmed A. Abd, Samia Allaoua Chelloug, Maali Alabdulhafith, and Mohamed Hammad. "Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data." Diagnostics 13, no. 7 (2023): 1216. http://dx.doi.org/10.3390/diagnostics13071216.

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Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making t

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Surendra, Kumar Shukla POONAM VERMA. "Using Convolutional Neural Networks, Arabic Handwritten Character Recognition." Scandinavian Journal of Information Systems 34, no. 2 (2023): 139–44. https://doi.org/10.5281/zenodo.7885780.

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Recognizing handwritten Arabic numbers is a challenging research topic. Impulsive by this research topic proposed two convolutional neural networks for recognizing Arabic handwritten numerals. Two proposed models have been analyzed using different filter sizes. The Arabic Number dataset exported from Kaggle was trained. The simplest proposed model achieved high recognition accuracy of 99.92%, outperforming the other complex with a more reasonable accuracy. For the MADBase dataset

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Putra, Muhammad Reza, and Azuraliza Abu Bakar. "Data Preprocessing: Case Study on monthly number of visitors to Taiwan by their residence and purpose." Jurnal KomtekInfo 7, no. 1 (2020): 1–14. http://dx.doi.org/10.35134/komtekinfo.v7i1.60.

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This paper will explain in details on data reports preliminary on dataset, how the pre-processing data mainly for data cleaning and reduction process applied to a dataset. The dataset that will be used is number of visitors to Taiwan by their residence and purpose.Dataset which is obtained based on kaggle, findings from Scraped from Taiwan Tourism Bureau. The surveys have been carried out using Foreign visitor data covers all foreign visitors directly arrived in Taiwan through the airports, ports and land.

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Yashwanth M and Laxmi B Rananavare. "Fake News Identification for Web Scrapped Data." Journal of Advanced Zoology 44, S6 (2023): 971–77. http://dx.doi.org/10.17762/jaz.v44is6.2329.

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Majority of the people get affected with misleading stories spread through different posts on social media and forward them assuming that it is a fact. Nowadays, Social media is used as a weapon to create havoc in the society by spreading fake news. Such havoc can be controlled by using machine-learning algorithms. Various methods of machine learning and deep learning techniques are used to identify false stories. There is a need for identification and controlling of fake news posts that have increased in alarming rate. Here we use Passive-Aggressive Classifier for fake news identification. Tw

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Mulyani, Heti, Ricak Agus Setiawan, Musawarman, and Annisa Romadloni. "Clustering Area Covid-19 Indonesia With K-Means (Case study : Kaggle Dataset)." Journal of Information Technology and Its Utilization 5, no. 2 (2022): 41–46. http://dx.doi.org/10.56873/jitu.5.2.4894.

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The spread of the coronavirus in Indonesia is quite fast. The spread of Covid 19 is almost evenly distributed in all provinces in Indonesia. Some areas even have a fairly high mortality rate. Therefore, it is necessary to group regions to find out which areas have the highest to lowest Covid cases so that the appropriate response process can be carried out. In addition, data visualization is also needed that provides information on COVID-19 data for each province. In this study, the data were grouped using the K-Means Clustering method. The dataset used is the Indonesian Covid-19 dataset from

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