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Artykuły w czasopismach na temat „NaiveBayes”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Arip Rahman Hakim and Achmad Udin Zailani. "SISTEM PERPUSTAKAAN BERBASIS WEB SMPN 226 JAKARTA DAN PENGOPTIMALAN PENCARIAN DENGAN NAIVEBAYES." JURNAL SATYA INFORMATIKA 7, no. 02 (2023): 93–104. http://dx.doi.org/10.59134/jsk.v7i02.175.

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Perpustakaan adalah perpustakaan dikenal sebagai sebuah koleksi besar yang dibiayai dan dioperasikan oleh sebuah kota atau institusi. Demikian halnya di lembaga pendidikan formal seperti sekolah, perpustakaan merupakan sarana wajib yang harus ada.Tetapi tidak semua sekolah mempunyai senuah sistem informasi yang dapat menyimpan dan mencatat pengolahan data buku. Berdasarkan hal itu penulis mencoba membuat sistem informasi yang mudah digunakan dengan berbasis website dan database. Tujuan dari penelitian ini adalah merancang sistem informasi perpustakaan dan menerapkan klasifikasi Naive Bayes unt

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Özdemir, Abdulkadir, Uğur Yavuz, and Fares Abdulhafidh Dael. "Performance evaluation of different classification techniques using different datasets." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3584. http://dx.doi.org/10.11591/ijece.v9i5.pp3584-3590.

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<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification te

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Abdulkadir, Özdemir, Yavuz Uğur, and Abdulhafidh Dael Fares. "Performance evaluation of different classification techniques using different datasets." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3584–90. https://doi.org/10.11591/ijece.v9i5.pp3584-3590.

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Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many processes such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. Many data users wasting a lot of time trying many classification techniques in order to find the most an appropriate technique to be used. This paper showing the differe

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Mohaddes Deylami, Hanif, and Yashwant Prasad Singh. "Cybercrime detection techniques based on support vector machines." Artificial Intelligence Research 2, no. 1 (2012): 1. http://dx.doi.org/10.5430/air.v2n1p1.

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This paper presents the cybercrime detection model by using support vector machines (SVMs) to classify social network (Facebook) dataset. We try to compare between three kinds of classification algorithms such as: SVMs, AdaBoostM1, and NaiveBayes in order to find a high percentage of classification accuracy. Finally, we conclude SVMs as the best classification algorithm, which uses different breeds of kernel functions in order to improve the classification accuracy on Facebook dataset. Besides, we are using the Weka 3.7.4 software to evaluate classifiers on Facebook dataset.

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孔, 梦秋. "Research on the Classification of Manufacturing Performance Evaluation in Guizhou Based on NaiveBayes Model." Advances in Applied Mathematics 08, no. 12 (2019): 2062–71. http://dx.doi.org/10.12677/aam.2019.812237.

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Sun, Pei Pei, Quan Yin Zhu, Lei Zhou, and Yong Jun Zhang. "Comparative Analysis of Text Categorizer on Science and Technology Intelligence." Applied Mechanics and Materials 530-531 (February 2014): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.502.

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In order to more effectively classify the science and technology intelligence text, the idea that classifying science and technology intelligence text categorization based on different classifiers is proposed. The experiment is done with two thousand Chinese texts based on three different classifiers in this paper. Among these classifiers, the rate of correctly classified instances with NaiveBayes Classifier is 96.95 percent and J48 Classifiers is 97.59. The highest of three classifiers is SMO Classifier and its correct rate is 98.65 percent. According to the analysis of experimental results,

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Rubio Delgado, Elayne, Lisbeth Rodríguez-Mazahua, José Antonio Palet Guzmán, et al. "Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks." Scientific Programming 2018 (2018): 1–21. http://dx.doi.org/10.1155/2018/4304017.

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This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web applicatio

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K P, Mamatha. "Customer Feedback Analysis Using Facial Emotion Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31022.

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Our lives are being significantly impacted by the rapid development of wireless technology and mobile gadgets on this day. The digital economy demands that services be developed almost instantly while also paying close attention to client feedback. It becomes difficult to manage and analyze the information gathered about products from customers. Also, everyone is not intended to provide clear feedback whether the product was satisfactory or not. It is a very difficult and time- consuming task to analyze the data collected manually. To proceed with the problem and through much research we came

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Maia, Luiz Cláudio, and Renato Rocha Souza. "Uso de sintagmas nominais na classificação automática de documentos eletrônicos." Perspectivas em Ciência da Informação 15, no. 1 (2010): 154–72. http://dx.doi.org/10.1590/s1413-99362010000100009.

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Esta pesquisa verificou se ocorre aprimoramento na classificação de documentos eletrônicos com o uso de técnicas e algoritmos de mineração de texto (análise de texto) utilizando-se, além das palavras, sintagmas nominais como indexadores. Utilizaram-se duas ferramentas nos experimentos propostos desta pesquisa o OGMA e a WEKA. O OGMA foi desenvolvido pelos autores para automatizar a extração dos sintagmas nominas e o cálculo do peso de cada termo na indexação dos documentos para cada um dos seis métodos propostos. A WEKA foi utilizada para analisar os resultados encontrados pelo OGMA utilizando

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Seimahura, Syarah, and Arina Selawati. "ANALISIS PERBANDINGAN KLASIFIKASI CITRA MYCROBACTERIUM TUBERCULOSIS." Akrab Juara : Jurnal Ilmu-ilmu Sosial 7, no. 1 (2022): 311. http://dx.doi.org/10.58487/akrabjuara.v7i1.1777.

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Tuberculosis (TB) is an infectious disease that remains a challenging health problem worldwide. This disease is caused by rod-shaped bacteria called Mycobacterium tuberculosis. These bacteria usually affect the lungs but can also spread to other parts of the body such as the eyes, bones and blood vessels. It was reported that around. 4.74 million new TB cases were identified and around eight hundred thousand people died from TB, during 2015 in Southeast Asia. In this study, color image segmentation techniques were carried out using classification methods by comparing several methods including

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Kurniawan, Budi, Achmad Suwarisman, Iis Afriyanti, Aditya Wahyudi, and Dedi Dwi Saputra. "Analisis Sentimen Complain dan Bukan Complain pada Twitter Telkomsel dengan SMOTE dan Naïve Bayes." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 7, no. 1 (2023): 106–13. http://dx.doi.org/10.35870/jtik.v7i1.691.

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Analisis ini bertujuan untuk mengetahui sentimen publik terhadap Telkomsel yang diposting di twitter Indonesia, yang menjadikan riset pasar tentang opini publik sangat berguna. Dataset diambil dari media sosial Twitter dalam query bahasa Indonesia dengan metode crawling menggunakan aplikasi RapidMiner dan hasil dari crawling data set tersebut terdapat 1000 tweet dengan sentiment komplain dan bukan komplain. Maka dari 1000 tweet akan dilakukan preprocessing dengan metode SMOTE Upsampling dan Naivebayes serta beberapa filtering seperti transform case, tokenize, filter tokenize (by length) stemmi

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Adhikari, Saurab. "Importance of Data Preprocessing and Parameters Tuning for Supervised Machine Learning Models on Tweets Sentiment Analysis." Batuk 10, no. 1 (2024): 133–51. http://dx.doi.org/10.3126/batuk.v10i1.62303.

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This paper shows the comparison of five different supervised machine learning models by showing the accuracy and classification report of these models when used for tweets sentiments analysis while showing the improvement in accuracy when data was preprocessed and parameters were tuned. The five different models that were used are: NaiveBayes, Support Vector Machine, Random Forest, Long Short-Term Memory (LSTM) and XG Boost. Total of 25000 tweets were processed, analyzed and predicted the output as positive, negative, or neutral using those models. This research would help to understand which

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Kumar Keshamoni, Dr L Koteswara Rao, Dr. D. Subba Rao. "Enhancing COVID-19 Diagnosis: A Multi-Modal Approach Utilizing the CNN Algorithm in Automated Applications." Journal of Advanced Zoology 44, S2 (2023): 2884–91. http://dx.doi.org/10.17762/jaz.v44is2.1477.

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Rapidly identifying COVID-19 patients is essential for effective disease control and management. To address this need, we have developed an automated application that utilizes multi-modal data, including Chest X-ray, Electrocardiogram (ECG), and CT scan images, for precise COVID-19 patient identification. This application comprises a two-stage process, starting with a web-based questionnaire and then the submission of medical images for verification. Leveraging various ML and DL techniques, including CNN, KNN, Logistic Regression, Decision Tree, and NaiveBayes, We conducted extensive model tra

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Surameery, Nigar M. Shafiq, and Dana Lattef Hussein. "Comparative Study of Classification Techniques For Large Scale Data - Case Study." Kurdistan Journal of Applied Research 2, no. 3 (2017): 56–61. http://dx.doi.org/10.24017/science.2017.3.2.

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The existence of Massive datasets that are generated in many applications provides various opportunities and challenges. Especially, scalable mining of such large-scale datasets is a challenging issue that attracted some recent research. In the present study, the main focus is to analyse the classification techniques using WEKA machine learning workbench. Moreover, a large-scale dataset was used. This dataset comes from the protein structure prediction field. It has already been partitioned into training and test sets using the ten-fold cross-validation methodology. In this experiment, nine di

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Li, Yanjuan, Zhengnan Zhao, and Zhixia Teng. "i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning." BioMed Research International 2021 (May 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/5515342.

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As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) plays a crucial role in controlling gene replication, expression, cell cycle, DNA replication, and differentiation. The accurate identification of 4mC sites is necessary to understand biological functions. In the paper, we use ensemble learning to develop a model named i4mC-EL to identify 4mC sites in the mouse genome. Firstly, a multifeature encoding scheme consisting of Kmer and EIIP was adopted to describe the DNA sequences. Secondly, on the basis of the multifeature encoding scheme, we developed a stacked ensemble mo

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Tang, Jiansong, Ryosuke Saga, Qiangsheng Dai, and Yingchi Mao. "Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches." Applied Sciences 13, no. 23 (2023): 12561. http://dx.doi.org/10.3390/app132312561.

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With the rise in meteorological disasters, improving evaluation strategies for disaster response agencies is critical. This shift from expert scoring to data-driven approaches is challenged by sample imbalance in the data, affecting accurate capability assessments. This study proposes a solution integrating adaptive focal loss into the cross-entropy loss function to address sample distribution imbalances, facilitating nuanced evaluations. A key aspect of this solution is the Encoder-Adaptive-Focal deep learning model coupled with a custom training algorithm, adept at handling the data complexi

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Talukder, Sajedul, and Faruk Hossen. "COVFILTER: A Low-cost Portable Device for the Prediction of Covid-19 for Resource-Constrained Rural Communities." International Journal of Artificial Intelligence & Applications 13, no. 02 (2022): 1–20. http://dx.doi.org/10.5121/ijaia.2022.13201.

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Early identification of COVID-19 is critical for preventing death and significant illness. People living in remote parts of resource-constrained countries find it more difficult to get tested due to a lack of adequate testing. As a result, having a primary filtering tool that can assist us in simplifying bulk COVID testing to prevent community spread is vital. In this paper, we introduce CovFilter, a low-cost portable device for COVID-19 prediction for resource-constrained rural communities, with the goal of encouraging people to be tested for COVID-19 in a more informed manner. CovFilter Hard

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Verma, Arpit, Mayank Pathak, Akhilesh Kumar Prajapati, and Harsh Srivastava. "Heart Disease Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1950–56. http://dx.doi.org/10.22214/ijraset.2024.59266.

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Abstract: One of the most common tasks inmachine learning is to classify data. Machine learning is a key feature to derive information from corporate operating datasets from large databases. Machine Learning in Medical Health Care is an essential emerging field for delivering prognosis anda deeper understanding of medical data. Most methods of machine learning depend on several features defining the behavior of the algorithm and influencing the output and the complexity of the resulting models directly or indirectly. In the last tenyears, heart disease is the world’s leading cause of death. Ma

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Qois Al’Ariq, Ginanjar Setyo Permadi, Chamdan Mashuri, and Anita Andriani. "IMPLEMENTASI NAIVE BAYES DALAM MEMPREDIKSI PENYAKIT DIABETES MELLITUS." Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi 9, no. 1 (2024): 144–53. https://doi.org/10.33752/inovate.v9i1.7267.

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Diabetes melitus adalah salah satu penyakit yang secara signifikan mempengaruhi populasi global.Penelitian ini mengeksplorasi penerapan Naive Bayes untuk memprediksi diabetes melitus. Permasalahanyang dihadapi antara lain kompleksitas data klinis, keragaman fitur, dan kebutuhan akan prediksi yangakurat. Solusi yang diusulkan adalah dengan menggunakan algoritma klasifikasi Naive Bayes yangmemanfaatkan asumsi sederhana namun kuat tentang independensi fitur. Sistem ini dijelaskan denganlangkah-langkah yang melibatkan pra-pemrosesan data, partisi kumpulan data, pelatihan model NaiveBayes, dan eval

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Er., Hari K.C. "ONLINE SOCIAL NETWORK ANALYSIS USING MACHINE LEARNING TECHNIQUES." International Journal of Advances in Engineering & Scientific Research 4, no. 4 (2017): 25–40. https://doi.org/10.5281/zenodo.10776039.

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<strong>Abstract: </strong> People spent most of the time in Social Networks.People express their views and opinions in Social Networks.Opinions influence the behaviors of the people. Opinion is the subject of study of Sentiment analysis and Opinion Mining. Opinion expressed in Social network can be analyzed and assist in making decision choosing the most popular brands. Sometime predicting the future results too. Twitter is the most popular Social networking site where peopletweet about particular topics. Different Machine Learning Algorithms such as Naive Bayes, Support Vector Machine and Lo

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AIRCC. "LEVERAGING NAIVE BAYES FOR ENHANCED SURVIVAL ANALYSIS IN BREAST CANCER." International Journal of Artificial Intelligence & Applications (IJAIA) 15, no. 4 (2024): 47–56. https://doi.org/10.5121/ijaia.2024.15402.

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The study aims to predict breast cancer survival using Naïve Bayes techniques by comparing differentmachine learning models on a comprehensive dataset of patient records. The main classification groupswere survival and non-survival. The objective was to assess the performance of the Naïve Bayes classifierin the field of data mining and to achieve significant results in survival classification, aligning with currentacademic research. The Naive Bayes classifier attained an average accuracy of 91.08%, indicating consistent performance,though with some variability across different folds.

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Syahputra, Indra Edy, Tulus Tulus, and Syahril Efendi. "Indonesian Text Dataset for Determining Sentiment Classification Using Mechine Learning Approach." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 192–201. http://dx.doi.org/10.31289/jite.v3i2.3153.

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Advances in information technology encourage the emergence of unlimited textual information with the use of online media developing so rapidly that the emergence of the need for information presentation without reducing the value of the information presented. Basicaly the concept of the dataset is a general form of almost every discipline, where the dataset provides empirical basic information for research activities. Sentiment analysis is done to see opinions or feelings about a problem or identify and classify information trends from the problem. The dataset analysis in determining sentiment

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Li, Shun, Liqing Cui, Changye Zhu, Baobin Li, Nan Zhao, and Tingshao Zhu. "Emotion recognition using Kinect motion capture data of human gaits." PeerJ 4 (September 15, 2016): e2364. http://dx.doi.org/10.7717/peerj.2364.

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Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker’s emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants’ gaits under neutral state, induced anger and induced happiness were recorded by two Ki

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Paul, Herdi, Anggri Sartika Wiguna, and Heri Santoso. "PENERAPAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES UNTUK KLASIFIKASI JENIS MOBIL TERLARIS BERDASARKAN PRODUKSI DI INDONESIA." JATI (Jurnal Mahasiswa Teknik Informatika) 7, no. 1 (2023): 39–44. http://dx.doi.org/10.36040/jati.v7i1.5555.

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Mobil merupakan salah satu alat transportasi yang paling banyak digunakan di Indonesia. Berdasarkan spesifikasinya, mobil memiliki berbagai varian atau jenis. Algoritma SVM yaitu untuk mencari hyperline yang berperan sebagai pemisah 2 buah class pada input space dan Naive Bayes merupakan salah satu metode klasifikasi yang menggunakan metode probabilitas dan statistik. Dari berbagai macam merek tersebut akan di bentuk sebuah Class, supaya produsen serta kosumen mampu untu mengenali merk dari sebuah mbil yang sangat terlaris di produksi berdasarkan output ataupun kategorinya GAIKINDO merupakan G

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Suárez Riveros, Lilian Daniela, Wilmer Pineda Ríos, and Iván Mauricio Mendivelso Ramírez. "Técnicas estadísticas y logro de aprendizaje: revisión bibliográfica." Eco Matemático 12, no. 2 (2021): 112–25. http://dx.doi.org/10.22463/17948231.3323.

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El objetivo de este escrito fue describir las diferentes técnicas estadísticas que han sido empleados para comprender o explicar el logro de aprendizaje, en estudiantes en diferentes niveles educativos. Desde el punto de vista teórico se consolidaron las categorías a priori, provenientes de las técnicas estadísticas (Modelos Multinivel, Modelos geoespaciales, Regresión, Clustering, Análisis Descriptivo, Redes Neuronales, Árboles de decisión, Bosques aleatorios, NaiveBayes y Support Vector Machine), así como la conceptualización de Logro de Aprendizaje. El enfoque metodológico para la revisión

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Et. al., B. Mukunthan. "Detection of Malicious Data in Twitter Using Machine Learning Approaches." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 4951–58. http://dx.doi.org/10.17762/turcomat.v12i3.2008.

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: Unlike traditional media social media is populated by unknown individuals who can broadcast whatever they like. This online social media culture is dynamic in its nature and transition to digital media is becoming a trend among people. In upcoming years the use of traditional media will decline, and the increasing use of Online Social Networks(OSNs) blur the actual information of the traditional media. The information generated by the authentic users gives useful information to the general users, on the other hand,Spammers spread irrelevant or misleading information that makes social media a

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Nausheen, S., Kumar M. Anil, and K. K. Amrutha. "SURVEY ON SENTIMENT ANALYSIS OF STOCK MARKET." International Journal of Research - Granthaalayah 5, no. 4 (2017): 69–75. https://doi.org/10.5281/zenodo.572298.

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Sentiment analysis has seen a tremendous growth in the past few years. Sentiment analysis or opinion mining is a process of collecting users’ opinion from user generated content. It has various applications, such as stock market prediction, products’ review collection, etc. a large amount of work has been done in this field by applying sentiment analysis to various applications. The main goal of this paper is to study the various methods used for sentiment analysis. Further we explain the overview of various related papers and their performances.

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Zhang, Yiguang, Kristen M. Altenburger, Poppy Zhang, Tsutomu Okano, and Shawndra Hill. "Node Attribute Prediction with Weighted and Directed Edges on Single and Multilayer Networks." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 1779–91. http://dx.doi.org/10.1609/icwsm.v18i1.31425.

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With the rapid development of digital platforms, users can now interact in endless ways from writing business reviews and comments to sharing information with their friends and followers. As a result, organizations have numerous digital social networks available for graph learning problems with little guidance on how to select the right graph or how to combine multiple edge types. For example, while user-to-user interactions are directed in nature, many graph learning approaches use the undirected version of the network. In this paper, we introduce edge direction, edge weight, and multi-relati

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Aljammal, Ashraf H., Salah Taamneh, Ahmad Qawasmeh, and Hani Bani Salameh. "Machine Learning Based Phishing Attacks Detection Using Multiple Datasets." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 05 (2023): 71–83. http://dx.doi.org/10.3991/ijim.v17i05.37575.

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Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated,

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Rezaei-Hachesu, Peyman, Taha Samad-Soltani, Ruhollah Khara, Mehdi Gheibi, and Nazila Moftian. "192: PREDICTION OF ASTHMA CONTROL LEVELS USING DATA MINING METHODS: AN EVIDENCE-BASED APPROACH." BMJ Open 7, Suppl 1 (2017): bmjopen—2016–015415.192. http://dx.doi.org/10.1136/bmjopen-2016-015415.192.

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Background and aims:Asthma is a chronic lung disease and has a raising worldwide prevalence. Lack of timely and appropriate control for this condition leads to financial and physical injuries. The aim of this study is to prediction of asthma control levels by applying data mining algorithms.Methods:This is a cross-sectional study carried out in the city of Sanandaj in Iran. Samples consist of 600 referred patient patients who live with asthma to Tohid pulmonary clinic in Sanandaj In a period of two months in 2015. Data were collected based on the study's inclusion criteria. Preprocessing was p

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Syed Amair Hussain, Gulam Junaid, Syed Abdul Wahab Asif3, and Dr.S.Md.Mazhar Ul Haq. "Brain Tumor Detection Using Convolutional Neural Network." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 593–99. https://doi.org/10.62647/ijitce2025v13i2spp593-599.

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Brain Tumor segmentation is one of the most crucialand arduous tasks in the field of medical imageprocessing as a human-assisted manualclassification can result in inaccurate predictionand diagnosis. Moreover, it becomes a tedious taskwhen there is a large amount of data present to beprocessed manually. Brain tumors have diversifiedappearance and there is a similarity between tumorand normal tissues and thus the extraction of tumorregions from images becomes complicated. In thisthesis work, we developed a model to extract braintumor from 2D Magnetic Resonance brain Images(MRI) by Fuzzy C-Means

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Noviyanto, Hendri, and Bayu Mukti. "Period Study Accuracy Prediction using Sequential Minimal Optimization Algorithm." SinkrOn 5, no. 1 (2020): 164–69. http://dx.doi.org/10.33395/sinkron.v5i1.10621.

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The study period is quite influential in the assessment of a university. The imbalance in the ratio of students to lecturers causes the quality of teaching and learning to decline, this is because one lecturer has to manage many students. Acquisition of accreditation scores and society's assumptions about higher education are also strongly influenced by the number of student graduations on time. Therefore, the prediction of the accuracy of the study period is needed as consideration for related parties to solve the problem of student learning delay. Sources of data in this study were taken fro

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Pawar, Kiran S., and Babasaheb J. Mohite. "Performance analysis of UKM-IDS20 dataset on machine learning algorithms." Journal of Statistics and Management Systems 27, no. 5 (2024): 997–1008. http://dx.doi.org/10.47974/jsms-1296.

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Machine learning algorithms are essential in classification and regression because they might a significant outcome on the accuracy of the classifier. It decreases the total of features of the traffic records. The algorithms adaptively improve their performance, reducing the processor and memory norms. This work proposes the machine learning algorithms on the available well-known mathematically proven classifiers. The proposed system for intrusion detection is tested and validated on UKM-IDS20 datasets with the suite of the classifiers. The system uses the BayesNet, Lazy Kstar, NaiveBayes, Nav

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Kalicińska, Jadwiga, Barbara Wiśniowska, Sebastian Polak, and Radoslaw Spiewak. "Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests—How Does the Infotechnomics Compare to Other “Omics” in the Cosmetics Safety Assessment?" International Journal of Molecular Sciences 24, no. 7 (2023): 6801. http://dx.doi.org/10.3390/ijms24076801.

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The aim of the current study was to develop an in silico model to predict the sensitizing potential of cosmetic ingredients based on their physicochemical characteristics and to compare the predictions with historical animal data and results from “omics”-based in vitro studies. An in silico model was developed with the use of WEKA machine learning software fed with physicochemical and structural descriptors of haptens and trained with data from published epidemiological studies compiled into estimated odds ratio (eOR) and estimated attributable risk (eAR) indices. The outcome classification wa

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Joshi, Ankur, Sukanya Sharma, N. V. M. Rao, and A. K. Vaish. "Usage of Machine Learning Algorithm Models to Predict Operational Efficiency Performance of Selected Banking Sectors of India." International Journal of Emerging Technology and Advanced Engineering 12, no. 6 (2022): 105–14. http://dx.doi.org/10.46338/ijetae0622_14.

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—It was an attempt to predict the impact of NPAs in the selected public (SBI, BoI, BoB, BoM, CBoI, AB, CB, AlB,) and private (AxB, ICB, HDFCB and KB) banking sectors from 2008 to 2019. The data was also used to predict operational performance efficiency of these banking sectors after extracting through machine learning (ML) algorithm models and statistical interpretation of prediction accuracy by using WEKA tool. We used different models viz. NaiveBayes (NB), BayesNet (BN), logistic regression (LgR), Sequential minimal optimization of Support Vector Machine regression (SMOreg), Linear Logistic

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Mahiddin, Normadiah, Zulaiha Ali Othman, and Nur Arzuar Abdul Rahim. "Interrelated Decision-Making Model for Diabetes." Asia-Pacific Journal of Information Technology and Multimedia 10, no. 02 (2021): 170–86. http://dx.doi.org/10.17576/apjitm-2021-1002-12.

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Diabetes is one of the growing chronic diseases. Proper treatment is needed to produce its effects. Past studies have proposed an Interrelated Decision-making Model (IDM) as an intelligent decision support system (IDSS) solution for healthcare. This model can provide accurate results in determining the treatment of a particular patient. Therefore, the purpose of this study is to develop a diabetic IDM to see the increased decision-making accuracy with the IDM concept. The IDM concept allows the amount of data to increase with the addition of data records at the same level of care, and the addi

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Silva, Luis Augusto, André Sales Mendes, Héctor Sánchez San Blas, Lia Caetano Bastos, Alexandre Leopoldo Gonçalves, and André Fabiano de Moraes. "Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning." Sensors 23, no. 1 (2022): 138. http://dx.doi.org/10.3390/s23010138.

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Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for ma

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Er., Hari K.C. "ONLINE SOCIAL NETWORK ANALYSIS USING MACHINE LEARNING TECHNIQUES." International Journal of Advances in Engineering & Scientific Research Vol.4, Issue 4, Jun-2017 (2017): pp 25–40. https://doi.org/10.5281/zenodo.846479.

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People spent most of the time in Social Networks.People express their views and opinions in Social Networks.Opinions influence the behaviors of the people. Opinion is the subject of study of Sentiment analysis and Opinion Mining. Opinion expressed in Social network can be analyzed and assist in making decision choosing the most popular brands. Sometime predicting the future results too. Twitter is the most popular Social networking site where peopletweet about particular topics. Different Machine Learning Algorithms such as Naive Bayes, Support Vector Machine and Logistic Regression are used t

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Qie, Shuai, Xin Zhang, Jiusong Luan, Zhelun Song, Jingyun Li, and Jingyu Wang. "Model development and validation for predicting small-cell lung cancer bone metastasis utilizing diverse machine learning algorithms based on the SEER database." Medicine 104, no. 12 (2025): e41987. https://doi.org/10.1097/md.0000000000041987.

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The aim of this study was to devise a machine learning algorithm with superior performance in predicting bone metastasis (BM) in small cell lung cancer (SCLC) and create a straightforward web-based predictor based on the developed algorithm. Data comprising demographic and clinicopathological characteristics of patients with SCLC and their potential BM were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2018. This data was then utilized to develop 12 machine learning algorithm models: support vector machine, logistic regression, NaiveBayes, extreme gra

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He, Shuaibing, Xuelian Zhang, Shan Lu, Ting Zhu, Guibo Sun, and Xiaobo Sun. "A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study." Biomolecules 9, no. 10 (2019): 577. http://dx.doi.org/10.3390/biom9100577.

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In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI,

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Kavuru.Pavani, Katam.Kusuma, and Hema Venkata Lakshmi Medicharla. "Analysing Amazon Product Reviews Using Machine Learning." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 2 (2024): 5. https://doi.org/10.5281/zenodo.10753941.

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Reviews from customers are now an integral part of most online e-commerce (Social media , amazon ,Flipkart, meesho) Companies for their daily operations. customers reviews are becoming a major factor in determining what consumer decide to buy. For analyzing the growth of e-commerce companies based on customer reviews or feedback. Organizations typically don’t have the time or resource to scour the internet and read and analyse every piece of data relating to their products, services and brand . Sentiment analysis is an important way for organizations to understand how customers perceive

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Raut Rahul Ganpat, Et al. "Unified Fake News Detection System (UFNDS) Framework." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 4243–57. http://dx.doi.org/10.17762/ijritcc.v11i9.9876.

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The deliberate spread of misleading or inaccurate material pose as authentic news is known as "fake news." Its increasing prevalence calls for the creation of practical strategies to recognize and counteract its negative effects on people and society. Previous methods of identifying fake news depended on linguistic signals and stylistic components. However, these methods faced limitations in terms of their applicability and accuracy. To overcome these constraints, this study proposes the utilization of an extended stacking ensemble classification algorithm (ES-ECA), a machine learning techniqu

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Marutho, Dhendra. "PERBANDINGAN METODE NAÃVE BAYES, KNN, DECISION TREE PADA LAPORAN WATER LEVEL JAKARTA." Jurnal Ilmiah Infokam 15, no. 2 (2019). https://doi.org/10.53845/infokam.v15i2.175.

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Tujuan dari penelitian ini adalah membandingkan antara metode Naivebayes, KNN, DecisionÂ Tree. Dimana data dari penelitan adalah data set laporan ketinggian air di Jakarta berasalÂ dari data.go.id, pada penelitian ini akan diukur confusion matrix, precision, recall, accuracy, hingga f-measure kemudian juga dihitung root mean squere error dari tiap-tiap metode, dariÂ perhitungan tersebut metode Decission tree mendapatkan accuracy tertinggi hingga 96.56%Â sehingga dapat disimpulkan metode klasifikasi decision tree lebih baik dari metodeÂ Naivebayes maupun KNNÂ

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"An Efficient Algorithm for NaiveBayes with Matrix Transposition." KIPS Transactions:PartB 11B, no. 1 (2004): 117–24. http://dx.doi.org/10.3745/kipstb.2004.11b.1.117.

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CEYHAN, İsmail Fatih. "İŞLETMELERDE FİNANSAL BAŞARISIZLIK ÖNGÖRÜSÜNDE MAKİNE ÖĞRENMESİNİN KULLANIMI: BİST UYGULAMASI." Finans Ekonomi ve Sosyal Araştırmalar Dergisi, September 28, 2023. http://dx.doi.org/10.29106/fesa.1359358.

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Çalışmanın amacı işletmelerin finansal başarısızlık riski ile ilgili tahmin yapay zekâ tekniklerinden makine öğrenmesi kullanılarak yapılmasıdır. Bu kapsamda, Borsa İstanbul Ulusal Pazar’da yer alan 14 firma ile Borsa İstanbul Yakın İzleme Pazarı’nda yer alan14 firmanın 2022 yılı 12 aylık gelir tabloları ve bilançolarından elde edilen 43 adet finansal oran kullanılmış makine öğrenmesi yöntemlerinden NaiveBayes, J48, RandomForest, LinearRegression, RandomTree kullanılmıştır. Şirketlerin mali tabloları kullanılarak elde edilen veriler ile, makine öğrenmesi uygulama modellerinden hangisinin daha

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"Prediction of Liver Disease patients using NaiveBayes and Random Forest 1." International Journal of Progressive Research in Engineering Management and Science, March 6, 2024. http://dx.doi.org/10.58257/ijprems32768.

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Pellucci, Paulo Roberto Simões, Renato Ribeiro de Paula, Walter Borges de Oliveira Silva, and Ana Paula Ladeira. "UTILIZAÇÃO DE TÉCNICAS DE APRENDIZADO DE MÁQUINA NO RECONHECIMENTO DE ENTIDADES NOMEADAS NO PORTUGUÊS." e-xacta 4, no. 1 (2011). http://dx.doi.org/10.18674/exacta.v4i1.305.

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Técnicas de Aprendizado de Máquina são comumente utilizados em tarefas que envolvem a identificação de padrões. O reconhecimento de entidades nomeadas consiste em identificar todos os nomes de um documento e classificá-los em categorias prévias. No presente artigo foram analisadas três técnicas com aprendizado supervisionado, sendo que, os melhores resultados foram obtidos com o algoritmo NaiveBayes. Inúmeros experimentos foram realizados usando a ferramenta Weka, no entanto, os valores obtidos por meio da estatística Kappa ficaram abaixo do esperado.

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"Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines." International Journal of Engineering and Advanced Technology 8, no. 6 (2019): 3697–706. http://dx.doi.org/10.35940/ijeat.f9377.088619.

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Preventing Chronic Kidney Disease has become one of the most intriguing task to the healthcare society. The major objective of this paper is to deal mainly with different classification algorithms namely NaiveBayes, Multi Layer Perceptron and Support Vector Machine. The work analyzes the Chronic Kidney Disease dataset taken from the machine learning repository of UCI. Pre-processing techniques such as missing value replacement, unsupervised discretization and normalization are applied to the Chronic Kidney Disease dataset to improve accuracy. Accuracy and time are the taken as the experimental

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Xu, Jing, Pan Qi, Xiaoyan Ou, et al. "Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer." Frontiers in Oncology 15 (May 29, 2025). https://doi.org/10.3389/fonc.2025.1607442.

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ObjectiveThis study aimed to evaluate the value of habitat analysis based bi-modal ultrasound radiomics in predicting axillary lymph node (ALN) status in patients with early-stage breast cancer, and find a non-invasive and accurate method to predict ALN status.Materials and methodsA total of 206 patients with 206 breast lesions were enrolled in this study from July 2019 to December 2023. All patients were randomly divided into training cohort (165 patients) and test cohort (41 patients). The feature extraction was manually delineated with ITK-SNAP software, while a K-means clustering algorithm

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Salal, Yass Khudheir, Samina Kausar, Silvia Gaftandzhieva, and Rositsa Doneva. "Activities to Predict Students’ Final Grades Based on Machine Learning Techniques." TEM Journal, May 27, 2025, 1438–44. https://doi.org/10.18421/tem142-44.

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The paper analyses a student performance dataset using the classification model. This approach can help improve the academic performance of struggling students by implementing specific procedures and strategies before exams, aiming to enhance educational attainment. To build a classification and ensemble models and predict student performance, a dataset with 120 instances was used, each of which has 39 attributes, with an implementation of NaiveBayes, Decision Tree, Neural network, k-Nearest Neighbors, and Support Vector Machine algorithms. This study's results offer valuable insights into stu

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