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Journal articles on the topic 'SMS spam filtering'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Mehta, Riya, and Ankita Gandhi. "A Survey: SMS Spam Filtering." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2672–77. http://dx.doi.org/10.31142/ijtsrd12850.

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2

Ma, Jialin, Yongjun Zhang, Zhijian Wang, and Kun Yu. "A Message Topic Model for Multi-Grain SMS Spam Filtering." International Journal of Technology and Human Interaction 12, no. 2 (April 2016): 83–95. http://dx.doi.org/10.4018/ijthi.2016040107.

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At present, content-based methods are regard as the more effective in the task of Short Message Service (SMS) spam filtering. However, they usually use traditional text classification technologies, which are more suitable to deal with normal long texts; therefore, it often faces some serious challenges, such as the sparse data problem and noise data in the SMS message. In addition, the existing SMS spam filtering methods usually consider the SMS spam task as a binary-class problem, which could not provide for different categories for multi-grain SMS spam filtering. In this paper, the authors propose a message topic model (MTM) for multi-grain SMS spam filtering. The MTM derives from the famous probability topic model, and is improved in this paper to make it more suitable for SMS spam filtering. Finally, the authors compare the MTM with the SVM and the standard LDA on the public SMS spam corpus. The experimental results show that the MTM is more effective for the task of SMS spam filtering.

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3

Delany, Sarah Jane, Mark Buckley, and Derek Greene. "SMS spam filtering: Methods and data." Expert Systems with Applications 39, no. 10 (August 2012): 9899–908. http://dx.doi.org/10.1016/j.eswa.2012.02.053.

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4

Ma, Jialin, Yongjun Zhang, Lin Zhang, Kun Yu, and Jinlin Liu. "Bi-Term Topic Model for SMS Classification." International Journal of Business Data Communications and Networking 13, no. 2 (July 2017): 28–40. http://dx.doi.org/10.4018/ijbdcn.2017070103.

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With the overflowing of Short Message Service (SMS) spam nowadays, many traditional text classification algorithms are used for SMS spam filtering. Nevertheless, because the content of SMS spam messages are miscellaneous and distinct from general text files, such as more shorter, usually including mass of abbreviations, symbols, variant words and distort or deform sentences, the traditional classifiers aren't fit for the task of SMS spam filtering. In this paper, the authors propose a Short Message Biterm Topic Model (SM-BTM) which can be used to automatically learn latent semantic features from SMS spam corpus for the task of SMS spam filtering. The SM-BTM is based on the probability of topic model theory and Biterm Topic Model (BTM). The experiments in this work show the proposed model SM-BTM can acquire higher quality of topic features than the original BTM, and is more suitable for identifying the miscellaneous SMS spam.

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5

Wu, Hongli, and Yong Hui Jiang. "SMS Spam Filtering Based on “Cloud Security”." Applied Mechanics and Materials 263-266 (December 2012): 2015–19. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2015.

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“Cloud Computing” technology has very big advantage in the computing power, scalability, reliability and cost etc. “Cloud Security "and " Cloud Storage " is one of the two main research fields. This paper puts forward “filter cloud” strategies of filter spam messages based on "Cloud Security" in order to achieve the purpose of filtering spam messages by addressing its root causes. It is a new attempt that applying “Cloud Computing” in spam messages filter and more mobile business would move to "cloud computing" platform in the future.

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DOGAN, Turgut. "On Term Weighting for Spam SMS Filtering." Sakarya University Journal of Computer and Information Sciences 3, no. 3 (December 30, 2020): 239–49. http://dx.doi.org/10.35377/saucis.03.03.735463.

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7

Zhang Ye. "The SMS spam filtering based on Adaboost." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 7 (April 15, 2013): 843–50. http://dx.doi.org/10.4156/aiss.vol5.issue7.99.

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Nagwani, Naresh Kumar, and Aakanksha Sharaff. "SMS spam filtering and thread identification using bi-level text classification and clustering techniques." Journal of Information Science 43, no. 1 (July 10, 2016): 75–87. http://dx.doi.org/10.1177/0165551515616310.

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SMS spam detection is an important task where spam SMS messages are identified and filtered. As greater numbers of SMS messages are communicated every day, it is very difficult for a user to remember and correlate the newer SMS messages received in context to previously received SMS. SMS threads provide a solution to this problem. In this work the problem of SMS spam detection and thread identification is discussed and a state of the art clustering-based algorithm is presented. The work is planned in two stages. In the first stage the binary classification technique is applied to categorize SMS messages into two categories namely, spam and non-spam SMS; then, in the second stage, SMS clusters are created for non-spam SMS messages using non-negative matrix factorization and K-means clustering techniques. A threading-based similarity feature, that is, time between consecutive communications, is described for the identification of SMS threads, and the impact of the time threshold in thread identification is also analysed experimentally. Performance parameters like accuracy, precision, recall and F-measure are also evaluated. The SMS threads identified in this proposed work can be used in applications like SMS thread summarization, SMS folder classification and other SMS management-related tasks.

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Abdulhamid, Shafi'I Muhammad, Muhammad Shafie Abd Latiff, Haruna Chiroma, Oluwafemi Osho, Gaddafi Abdul-Salaam, Adamu I. Abubakar, and Tutut Herawan. "A Review on Mobile SMS Spam Filtering Techniques." IEEE Access 5 (2017): 15650–66. http://dx.doi.org/10.1109/access.2017.2666785.

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10

Taufiq Nuruzzaman, M., Changmoo Lee, Mohd Fikri Azli bin Abdullah, and Deokjai Choi. "Simple SMS spam filtering on independent mobile phone." Security and Communication Networks 5, no. 10 (June 21, 2012): 1209–20. http://dx.doi.org/10.1002/sec.577.

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11

Hanif, Ellya Izatty, Cik Feresa Mohd Foozy, Isredza Rahmi A. Hamid, Rozlini Mohamed, Munirah Mohd Yusof, Ruhaya Ab Aziz, and Palaniappan Shamala. "Malay SMS Spam Detection Tool Using Keyword Filtering Technique." Journal of Physics: Conference Series 1793, no. 1 (February 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/1793/1/012064.

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12

Jalal Mussa, Diyari, and Noor Ghazi M. Jameel. "Relevant SMS Spam Feature Selection Using Wrapper Approach and XGBoost Algorithm." Kurdistan Journal of Applied Research 4, no. 2 (November 21, 2019): 110–20. http://dx.doi.org/10.24017/science.2019.2.11.

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In recent years with the widely usage of mobile devices, the problem of SMS Spam increased dramatically. Receiving those undesired messages continuously can cause frustration to users. And sometimes it can be harmful, by sending SMS messages containing fake web pages in order to steal users’ confidential information. Besides spasm number of hazardous actions, there is a limited number of spam filtering software. According to this paper, XGBoost algorithm used for handling SMS spam detection problem. Number of structural features was collected from previous studies. 15 structural features were extracted from Tiago’s dataset, which is the most frequently used dataset by researchers. For selecting the optimal relevant features, two different types of wrapper feature selection algorithms were used in order to reduce and select best relevant features. The accuracy and performance obtained by the selected features via sequential backward selection method was better comparing to sequential forward selection method. The extracted nine optimal features can be a good representation of a spam SMS message. Additionally, the classification accuracy obtained by the proposed method using nine optimal features with XGBoost algorithm is 98.64 using 10-fold cross validation.

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13

Utami, Lila Dini, Lestari Yusuf, and Dini Nurlaela. "Komparasi Algoritma Naïve Bayes dan Support Vectors Machine pada Analisis Sentimen SMS HAM dan SPAM." Infotek : Jurnal Informatika dan Teknologi 4, no. 2 (July 31, 2021): 249–58. http://dx.doi.org/10.29408/jit.v4i2.3665.

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SMS is a form of communication in the form of messages sent using mobile phones between the designated numbers. SMS is now rarely used because many of the features that have changed are used by chat applications. However, the SMS feature was not removed for one thing, official messages from various applications for leveraging or other official information still use SMS as a sign that the phone number used is there. However, since 2011 there have been so many misuses of this function, so it is suspected that many frauds use SMS as a tool to influence victims. This sms category goes to SMS spam. Therefore, SMS needs to be classified so that users can find out that the SMS is included in the category of Spam or ham (the opposite of spam). Using 400 datasets taken from the UCI repository which is divided into two classes, namely spam and ham, we compare two classification methods, namely Naive Bayes and Support vector Machine in order to get SMS filtering correctly. And after the calculations are done, the accuracy is obtained in Naive Bayes, which is 90.00% Support Vector Machine 81.00%.

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14

ZHU, Wu-hui, and Mei-qing WANG. "Spam phone number filtering method based on SMS submission pattern." Journal of Computer Applications 32, no. 12 (May 29, 2013): 3565–68. http://dx.doi.org/10.3724/sp.j.1087.2012.03565.

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15

赵, 彩迪. "Research on Naive Bayesian Spam SMS Filtering Based on MapReduce." Computer Science and Application 06, no. 07 (2016): 443–50. http://dx.doi.org/10.12677/csa.2016.67054.

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16

Lu, Li, Xiangui Xue, and Tao Li. "Application of Bayes Classification method in mobile phone spam short message filtering system." MATEC Web of Conferences 232 (2018): 01002. http://dx.doi.org/10.1051/matecconf/201823201002.

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The paper discussed the use of Bayes classification method in filtration system of short message spam (SMS). The method can classify the content of SMS, thus realizing effective filtering. Finally the paper carried out the result analysis and the appraisal of the Bayes classification model, which testified the model has some actually feasibility and extensibility.

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17

Ezpeleta, Enaitz, Iñaki Garitano, Urko Zurutuza, and José María Gómez Hidalgo. "Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 175–89. http://dx.doi.org/10.1142/s0218488517400177.

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Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.

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18

Kang, Seung-Shik. "A Normalization Method of Distorted Korean SMS Sentences for Spam Message Filtering." KIPS Transactions on Software and Data Engineering 3, no. 7 (July 31, 2014): 271–76. http://dx.doi.org/10.3745/ktsde.2014.3.7.271.

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19

Qin, Jian, and Shi Qun Yin. "Research of Chinese Multi-Pattern Fuzzy Matching Method for SMS Spam Monitoring." Advanced Materials Research 532-533 (June 2012): 748–52. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.748.

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In this paper, it is analyzed the content and achieving mechanism of WM algorithm on account of frequently asked questions during the monitoring and filtering of Short Message Service(SMS) spam, and brought forward a kind of Chinese message with multi- patterns fuzzy matching method to improve WM algorithm to apply to Chinese spam message filtering. The method firstly does pretreatment of key words and fuzzification of monitor for message on account of expressive diversity of Chinese message, and then realizes initial matching by improving WM algorithm. Because the preliminary result is not the final one, it needs further fuzzily match for the result. It does particular introduction of the method and the model of monitor and filtration for Chinese SMS spam in the paper. The method’s correctness and performing efficiency also has been done experimental analysis, test and comparison experiment with former system that had not been done pretreatment. Experimental results show the validity of the improving algorithms. The method is testing further during the practice.

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20

Ojugo, Arnold Adimabua, and David Ademola Oyemade. "Boyer Moore string-match framework for a hybrid short message service spam filtering technique." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 519. http://dx.doi.org/10.11591/ijai.v10.i3.pp519-527.

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Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.

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21

Xia, Tian, and Xuemin Chen. "A weighted feature enhanced Hidden Markov Model for spam SMS filtering." Neurocomputing 444 (July 2021): 48–58. http://dx.doi.org/10.1016/j.neucom.2021.02.075.

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22

Bouarara, Hadj Ahmed, Reda Mohamed Hamou, and Abdelmalek Amine. "New Bio Inspired Techniques in the Filtering of Spam." Journal of Information Technology Research 9, no. 2 (April 2016): 47–77. http://dx.doi.org/10.4018/jitr.2016040103.

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The internet era promotes electronic commerce and facilitates access to many services. In today's digital society the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This paper deals on the unveiling of fresh bio-inspired techniques (artificial social cockroaches (ASC), artificial haemostasis system (AHS) and artificial heart lungs system (AHLS)) and their application for SPAM detection. For the authors' experimentation, they have used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy and error). They have optimising the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine learning algorithms (decision tree C4.5 and K-means).

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23

Rani, T. Jhansi, T. Jaya Vumesh, P. Saiteja, V. Ajay Kumar Reddy, and M. Meghana. "SMS Spam Detection Framework Using Machine Learning Algorithms and Neural Networks." International Journal of Computer Science and Mobile Computing 10, no. 6 (June 30, 2021): 10–19. http://dx.doi.org/10.47760/ijcsmc.2021.v10i06.002.

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In our current generation we are very much habituated to many mobile services like communication, ecommerce etc. In mobile communication services SMS’s (Short Message Service’s) are very common and important services which we are using in personal purposes and profession. In these services some messages may cause spam attacks which is trap to users to access their personal information or attracting them to purchase a product from unauthorized websites. It is very easy for companies send any information or service or alert to their customers/users with these SMS API’s. Based on these services it is also possible for sending spam messages. So in this system we are using advance Machine Learning concepts for detection of the spam filtering in the SMS’s. In this system we are importing the dataset from UCI repository and for spam SMS detection we implementing machine learning classifiers like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Networks (NN) algorithms and with their metrics like accuracy, precision, recall and f-score. We calculate performances between there algorithms as well as we show the experiment results with visualization techniques and analyses which algorithm is best for spam SMS detection.

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24

Alshahrani, Ali. "Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 16 (August 23, 2021): 52. http://dx.doi.org/10.3991/ijim.v15i16.24197.

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<p class="0abstract">SMS spam messages represent one of the most serious threats to current traditional networks. These messages have been particularly prevalent overseas and are harmful to various types of devices. The current filtering scheme employed in conventional systems is unable to expose a large number of messages. To resolve this issue, a new intelligent security system is proposed to reduce the number of spam messages. It can detect novel spam messages that have a direct and negative impact on networks. The proposed system is heavily based on machine learning to explore various types of messages. The primary achievement of our study is the increase in the accuracy ratio as well as the reduction in the number of false alarms. According to the experimental results, it is clear that our system can realize outstanding results, detecting a massive number of massages.</p>

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25

Poorshahsavari, Maryam, and Omid Pourgalehdari. "Enhancing the Rate of Accuracy and Precision in Spam Filtering in Farsi SMS." Communications on Applied Electronics 3, no. 3 (October 23, 2015): 25–27. http://dx.doi.org/10.5120/cae2015651906.

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26

Almeida, Tiago A., Tiago P. Silva, Igor Santos, and José M. Gómez Hidalgo. "Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering." Knowledge-Based Systems 108 (September 2016): 25–32. http://dx.doi.org/10.1016/j.knosys.2016.05.001.

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27

Aragão, Marcelo V. C., Edielson Prevato Frigieri, Carlos A. Ynoguti, and Anderson P. Paiva. "Factorial design analysis applied to the performance of SMS anti-spam filtering systems." Expert Systems with Applications 64 (December 2016): 589–604. http://dx.doi.org/10.1016/j.eswa.2016.08.038.

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28

Onashoga, Adebukola S., Olusola O. Abayomi-Alli, Adesina S. Sodiya, and David A. Ojo. "An Adaptive and Collaborative Server-Side SMS Spam Filtering Scheme Using Artificial Immune System." Information Security Journal: A Global Perspective 24, no. 4-6 (September 2015): 133–45. http://dx.doi.org/10.1080/19393555.2015.1078017.

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29

She, Xue Bing. "Incremental-Learning Spam Messages Distinguishing and Sorting System Based on Arm Platform." Applied Mechanics and Materials 602-605 (August 2014): 3843–45. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3843.

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Now that the endless spam messages have sevely affected people on their works and daliy lives. The omission and even the help of the serrvice providrs (SP) in this regard make it extremely necessary to distinguish and sort the spam messages through filtering the messages on user’s mobile phone. The paper expatiated on a “self-study spam messages distinguishing and soring system’ developed onARM9 platform. By adding a spam box to SMS software of cellphome besidrs all os its normal functions, the system incessantly adjusts the weight of the featues though incremental learning method so as to achieve the highly accurate discrimination on whether a message received is the spam one or not, and futher decides to sort the message to the message box or the spam box, Test result on ARM9 platform shows, our technology can be applied completely to the mobile phones with just general performance.

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30

Ahn, Hye-yeong, Wan-zee Cho, and Jong-woo Lee. "Implementation of A Mobile Application for Spam SMS Filtering Using Set-Based POI Search Algorithm." Journal of Digital Contents Society 16, no. 5 (October 31, 2015): 815–22. http://dx.doi.org/10.9728/dcs.2015.16.5.815.

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31

Shahi, Tej Bahadur, and Abhimanu Yadav. "Mobile SMS Spam Filtering for Nepali Text Using Naïve Bayesian and Support Vector Machine." International Journal of Intelligence Science 04, no. 01 (2014): 24–28. http://dx.doi.org/10.4236/ijis.2014.41004.

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32

Kaya, Yilmaz, and Ömer Faruk Ertuğrul. "A novel feature extraction approach in SMS spam filtering for mobile communication: one-dimensional ternary patterns." Security and Communication Networks 9, no. 17 (October 19, 2016): 4680–90. http://dx.doi.org/10.1002/sec.1660.

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33

El Boujnouni, Mohamed. "SMS Spam Filtering Using N - gram method, Information Gain Metric and an Improved Version of SVDD Classifier." Journal of Engineering Science and Technology Review 10, no. 1 (February 2017): 131–37. http://dx.doi.org/10.25103/jestr.101.18.

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34

Gao, Bibu, and Wenqiang Zhang. "A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering." Applied Sciences 11, no. 14 (July 9, 2021): 6350. http://dx.doi.org/10.3390/app11146350.

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As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for combating spam. This paper first discusses the text fields, which possibly contain spam information, then use the hidden Markov model (HMM) to weight the fields and finally use convolutional neural network (CNN) to classify the RCS messages. In the HMM step, the text fields are treated differently. The short texts of these fields are represented as feature weight sequences extracted by a feature extraction algorithm based on a probability density function. Then, the proposed HMM learns the weight sequence and produces a proper weight for each short text. Other text fields with fewer words are also weighted by the feature extraction algorithm. In the CNN step, all these feature weights first construct the RCS message matrix. The matrices of the training RCS messages are used as the CNN model inputs for learning and the matrices of testing messages are used as the trained CNN model inputs for RCS message property prediction. Four optimization technologies are introduced into the CNN classification process. Promising experiment results are achieved on the real industrial data.

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Kim, Sin-Eon, Jung-Tae Jo, and Sang-Hyun Choi. "SMS Spam Filterinig Using Keyword Frequency Ratio." International Journal of Security and Its Applications 9, no. 1 (January 31, 2015): 329–36. http://dx.doi.org/10.14257/ijsia.2015.9.1.31.

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36

Selo W.T., Gilang Jalu, and Budi Susanto. "IMPLEMENTASI NAÏVE BAYESIAN CLASSIFIER UNTUK KASUS FILTERING SMS SPAM." Jurnal Informatika 9, no. 2 (July 9, 2014). http://dx.doi.org/10.21460/inf.2013.92.317.

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In 2011, the circulation of SMS spam in Indonesia was rampant. The SMS can contain promotion of a product which is often unsolicited by the recipient or fraud. This is an overlooked issue in Indonesia. But spam has been a very common topic in other countries. To resolve these problems, we need a system that can recognize SMS spam so the SMS can be diverted or marked prior to the user. In this research, we built a system that implementing the Naive Bayesian classifier for classifying SMS spam, so the user can recognize the SMS spam. The result of this research, the system built is able to classify a SMS into categories spam and not spam. Naïve Bayesian classifier can be implemented effectively in the case of SMS spam filtering. The proper use of text preprocessing can improve the performance of this classification system.

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37

Liu, Wuying, and Ting Wang. "Index-based Online Text Classification for SMS Spam Filtering." Journal of Computers 5, no. 6 (June 1, 2010). http://dx.doi.org/10.4304/jcp.5.6.844-851.

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38

"SMS Spam Detection using Tokenization and Feature Engineering." International Journal of Recent Technology and Engineering 8, no. 3 (September 30, 2019): 6805–7. http://dx.doi.org/10.35940/ijrte.c5736.098319.

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The enormous development of innovation and mobiles, the clients have been exposed to more spam messages than any other time in recent memory ever. SMS spam separating is a nearly an exceptionally ongoing answer for arrangement with such a significant issue.. This paper moves us to chip away at the assignment of separating versatile spam messages as whether it is Ham or Spam for the clients by adding messages to the worldwide accessible SMS dataset. The paper plans to break down various AI classifiers on huge corpus of SMS messages for the individuals around the globe. This paper also informs or tells the readers about the existing algorithms and it’s inefficiency in filtering the ham messages from spam messages. This paper makes use of tokenization to create tokens which are then fed into the feature engineering model to extract features and then to predict the outcome

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39

Gomaa, Wael Hassan. "The Impact of Deep Learning Techniques on SMS Spam Filtering." International Journal of Advanced Computer Science and Applications 11, no. 1 (2020). http://dx.doi.org/10.14569/ijacsa.2020.0110167.

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40

Uysal, A. K., S. Gunal, S. Ergin, and E. Sora Gunal. "The Impact of Feature Extraction and Selection on SMS Spam Filtering." Electronics and Electrical Engineering 19, no. 5 (May 15, 2013). http://dx.doi.org/10.5755/j01.eee.19.5.1829.

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41

Palimote, J., V. I. E. Anireh, and N. D. Nwiabu. "A Model for Filtering Spam SMS Using Deep Machine Learning Technique." IJARCCE 10, no. 4 (April 30, 2021). http://dx.doi.org/10.17148/ijarcce.2021.10403.

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