Mobile SMS Spam Filtering for Nepali Text Using Naïve Bayesian and Support Vector Machine

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International Journal of Intelligence Science

ISSN Print: 2163-0283
ISSN Online: 2163-0356
www.scirp.org/journal/ijis
E-mail: ijis@scirp.org

Google-based Impact Factor: 0.58
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"Mobile SMS Spam Filtering for Nepali Text Using Naïve Bayesian and Support Vector Machine"
written by Tej Bahadur Shahi, Abhimanu Yadav,
published by International Journal of Intelligence Science, Vol.4 No.1, 2014

has been cited by the following article(s):

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[9]	Comparative Analysis of Nepali News Classification using LSTM, Bi-LSTM and Transformer Model
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[16]	Memetic algorithm for short messaging service spam filter using text normalization and semantic approach
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[17]	Sentiment analysis in Nepali: Exploring machine learning and lexicon-based approaches
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[18]	Application of Machine Learning Classification to Detect Fraudulent E-wallet Deposit Notification SMSes
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[19]	Using an Artificial Neural Network to Improve Email Security
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[20]	EVALUATION OF MACHINE LEARNING ALGORITHMS FOR FILTERING AND ISOLATING SPAMMED MESSAGES
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[22]	An Empirical Study on the Classification of Chinese News Articles by Machine Learning and Deep Learning Techniques
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[23]	Comparative Study of Clustering Algorithms for Nepali News
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[24]	Improving Performance of Mobile SMS Classification Using TF-IDF & Multinational Naive Bayes Classifier
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[25]	Nepali news classification using Naïve Bayes, Support Vector Machines and Neural Networks
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[26]	Building Bi-lingual Anti-Spam SMS Filter
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[27]	Identifikasi SMS SPAM Berbahasa Indonesia Menggunakan Algoritma Support Vector Machine
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[28]	基于词向量和卷积神经网络的垃圾短信识别方法
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[29]	KLASIFIKASI PESAN SMS MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN SELEKSI FITUR GENETIC ALGORITHM
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[30]	Nepali SMS filtering Using Decision Trees, Neural Network and Support Vector Machine
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[31]	Nepali Multi-Class Text Classification
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[32]	arabic sms spam detection based on semantic classification
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[33]	Optimizing Naïve Bayes Algorithm for SMS Spam Filtering on Mobile Phone to Reduce the Consumption of Resources
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[34]	Identifikasi SMS Spam bahasa Indonesia Menggunakan Algoritma Twitter-LDA
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[35]	Rule Based Sentiment Analysis of Punjabi Tweets Using Vector Evaluation Method
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[36]	Cybercrime: Suspicious Viber Messages Detec-tion Model
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[39]	Cybercrime: Suspicious Viber Messages Detection Model
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[40]	A New SMS Spam Detection Method Using Both Content-Based and Non Content-Based Features
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[41]	Improving Nepali Document Classification by Neural Network
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[42]	The performance of soft computing techniques on content-based SMS spam filtering
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[43]	Efficient Support Vector Machines for Spam Detection: A Survey
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[44]	Implementasi Dan Analisis Klasifikasi Spam Pada Pesan Singkat Seluler Dengan Pendekatancollaborative Filtering Menggunakan Naïve Bayes
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[45]	ALGORITMA NAїVE BAYES DAN METODE UNTUK KLASIFIKASI SMS BERBAHASA INDONESIA

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[2]	Accurate SMS Spam Detection Using Support Vector Machine in Comparison with Logistic Regression 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 2023 DOI:10.1109/ICONSTEM56934.2023.10142266

[3]	Performance evaluation of Spam and Non-Spam E-mail detection using Machine Learning algorithms 2022 International Conference on Electronics and Renewable Systems (ICEARS), 2022 DOI:10.1109/ICEARS53579.2022.9752202

[4]	BERT Against Social Engineering Attack: Phishing Text Detection 2022 IEEE International Conference on Electro Information Technology (eIT), 2022 DOI:10.1109/eIT53891.2022.9813922

[5]	Incorporating a Topic Model into a Hypergraph Neural Network for Searching-Scenario Oriented Recommendations Applied Sciences, 2022 DOI:10.3390/app12157387

[6]	Pseudo Base Station Spam SMS Identification Based on BiLSTM-Attention 2022 11th International Conference on Communications, Circuits and Systems (ICCCAS), 2022 DOI:10.1109/ICCCAS55266.2022.9825128

[7]	Research Anthology on Artificial Neural Network Applications 2022 DOI:10.4018/978-1-6684-2408-7.ch071

[8]	Natural language processing for Nepali text: a review Artificial Intelligence Review, 2021 DOI:10.1007/s10462-021-10093-1

[9]	Social Network Users Create Seismic Intensity Maps: An Automatic Approach of the Methodology SN Computer Science, 2021 DOI:10.1007/s42979-021-00728-z

[10]	Sentiment analysis in Nepali: Exploring machine learning and lexicon-based approaches Journal of Intelligent & Fuzzy Systems, 2020 DOI:10.3233/JIFS-179884

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[12]	An Empirical Study on the Classification of Chinese News Articles by Machine Learning and Deep Learning Techniques 2019 International Conference on Machine Learning and Cybernetics (ICMLC), 2019 DOI:10.1109/ICMLC48188.2019.8949309

[13]	Nepali news classification using Naïve Bayes, Support Vector Machines and Neural Networks 2018 International Conference on Communication information and Computing Technology (ICCICT), 2018 DOI:10.1109/ICCICT.2018.8325883

[14]	Nepali SMS filtering Using Decision Trees, Neural Network and Support Vector Machine 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018 DOI:10.1109/ICACCCN.2018.8748286

[15]	Advanced Computer and Communication Engineering Technology Lecture Notes in Electrical Engineering, 2016 DOI:10.1007/978-3-319-24584-3_43

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