Relevant bibliographies by topics / Spam SMS

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spam SMS'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Spam SMS"

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Prachi, Ms Akshara, Mrs Ankita Gandhi, Mr Garv Kavdia, and Mr Deepak Parmar. "Spam SMS Classification." International Journal of Research Publication and Reviews 6, no. 3 (2025): 5956–61. https://doi.org/10.55248/gengpi.6.0325.12109.

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

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Suprihati, Ferin Reviantika. "Analisis Klasifikasi SMS Spam Menggunakan Logistic Regression." Jurnal Sistem Cerdas 4, no. 3 (2021): 155–60. http://dx.doi.org/10.37396/jsc.v4i3.166.

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SMS or Short Message Service is usually found on cell phones. SMS is divided into two categories, namely SMS spam and SMS non-spam (ham). Spam SMS is an SMS that is annoying to phone users because it tends to contain messages that are not important such as promos and scams. Meanwhile, non-spam SMS (ham) tend to contain important SMS, such as messages from previous users. In this study, the classification of spam SMS and non-spam SMS (ham) was carried out using the logistic regression method. The purpose of this study is to distinguish or classify between spam and non-spam SMS (ham). The datase
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Jaya Saputra, Nesan. "Analysis of SMS Spam Detection using Tf-Idf: A Study On SMS Spam Collection Dataset." Jurnal Sosial Teknologi 4, no. 4 (2024): 213–17. http://dx.doi.org/10.59188/jurnalsostech.v4i4.1214.

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This study explores the detection of SMS spam utilizing TF-IDF analysis on a dataset containing a collection of text messages labeled as spam or ham (non-spam). The dataset comprises messages suitable for spam detection analysis using TF-IDF techniques. The research aims to evaluate the effectiveness of TF-IDF in distinguishing between spam and spam (non-spam) messages. The analysis involves examining the precision, recall, and F1-score metrics to assess the performance of the classification model. The results demonstrate promising outcomes, with a high accuracy rate achieved in classifying sp
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Riya, Mehta, and Gandhi Ankita. "A Survey SMS Spam Filtering." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2672–77. https://doi.org/10.31142/ijtsrd12850.

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Now a days Short Message Service SMS is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service SMS spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques a
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Choudhary, Esha. "Spam SMS Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6868–76. http://dx.doi.org/10.22214/ijraset.2023.53235.

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Abstract: As the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. I
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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 (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 p
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Hameed, Sarab M. "Differential evolution detection models for SMS spam." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 596. http://dx.doi.org/10.11591/ijece.v11i1.pp596-601.

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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental
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Sarab, M. Hameed. "Differential evolution detection models for SMS spam." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 596–601. https://doi.org/10.11591/ijece.v11i1.pp596-601.

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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental
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Liu, Xiaoxu, Haoye Lu, and Amiya Nayak. "A Spam Transformer Model for SMS Spam Detection." IEEE Access 9 (2021): 80253–63. http://dx.doi.org/10.1109/access.2021.3081479.

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Dissertations / Theses on the topic "Spam SMS"

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Fredborg, Johan. "Spam filter for SMS-traffic." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94161.

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Communication through text messaging, SMS (Short Message Service), is nowadays a huge industry with billions of active users. Because of the huge userbase it has attracted many companies trying to market themselves through unsolicited messages in this medium in the same way as was previously done through email. This is such a common phenomenon that SMS spam has now become a plague in many countries. This report evaluates several established machine learning algorithms to see how well they can be applied to the problem of filtering unsolicited SMS messages. Each filter is mainly evaluated by an
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Bäckman, David. "EVALUATION OF MACHINE LEARNING ALGORITHMS FOR SMS SPAM FILTERING." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163188.

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The purpose of this thesis is to evaluate different machine learning algorithms and methods for text representation in order to determine what is best suited to use to distinguish between spam SMS and legitimate SMS. A data set that contains 5573 real SMS has been used to train the algorithms K-Nearest Neighbor, Support Vector Machine, Naive Bayes and Logistic Regression. The different methods that have been used to represent text are Bag of Words, Bigram and Word2Vec. In particular, it has been investigated if semantic text representations can improve the performance of classification. A tota
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Jaroš, Ján. "Detekce nevyžádaných zpráv v mobilní komunikaci a na sociálních sítích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236082.

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This thesis deals with spam in mobile and social networks. It focuses on spam in SMS messages and web service Twitter. Theoretical part provides brief overview of those two media, informations about what spam is, how to defend against it and where does it comes from. There is also a list of methods for spam detection, many of them have their roots in filtration of email communication. The rest of thesis is about design, implementation of application  for spam detection in SMS and Twitter messages and evaluation of its performance.
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Silva, Tiago Pasqualini da. "Normalização textual e indexação semântica aplicadas da filtragem de SMS spam." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8811.

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Submitted by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T17:49:19Z No. of bitstreams: 1 SILVA_Tiago_2016.pdf: 13631569 bytes, checksum: 7774c3913aa556cc48c0669f686cd3b5 (MD5)<br>Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T17:49:26Z (GMT) No. of bitstreams: 1 SILVA_Tiago_2016.pdf: 13631569 bytes, checksum: 7774c3913aa556cc48c0669f686cd3b5 (MD5)<br>Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T17:49:32Z (GMT) No. of bitstreams: 1 SILVA_Tiago_2016.pdf: 13631569 bytes, checksum: 7774c3913aa556cc48c0669f686cd3b5
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Kovařík, David. "Automatická identifikace šablony generující spam kampaně." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385921.

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Spam se typicky nevyskytuje ve formě samostatných zpráv, ale často bývá sdružován do takzvaných kampaní. Ty bývají automaticky generovány pomocí šablon. Díky tomu jsou jednotlivé zprávy sémanticky, ale ne syntakticky, ekvivalentní. Cílem práce je navrhnout algoritmus schopný z množiny zpráv jedné kampaně zpětně extrahovat šablonu, ze které tyto zprávy byly generovány. Práce se zaměřuje na spam v SMS komunikaci, ale navržené postupy jsou dostatečně obecné pro širší použití. Algoritmus je postaven na metodě zarovnávání dvou sekvencí, používané v bioinformatice pro nalezení podobných oblastí prot
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Bengtsson, Lisa. "How advertisement can affect attitudes - A qualitative study of how attitudes are towards advertisement through SMS and email." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20595.

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Det ämne som undersöks i denna uppsats är attityden mot reklamutskick via SMS och e-post. Syftet är att undersöka vilka faktorer som bidrar till attityden och om åldern spelar in som en faktor och om attityden är densamma till reklamutskick via SMS som den är via e-post. En enkätundersökning har genomförts med 139 respondenter som låg till grund för den kvalitativa fokusgruppsundersökning som den största delen av resultatet bygger på. Enkäten har besvarats av individer runt om i Skåne som är i ålder 20 år och uppåt. Enkäten skapades för att kunna identifiera problem och perspektiv inom ämnet s
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Fabrici, Alberto <1994&gt. "Industry 4.0 in Italian SMEs: the Pezzutti Group Spa Case." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/18546.

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Lo scopo dell'elaborato è comparare i dati sulle implementazioni di Industria 4.0 nelle PMI italiane raccolte da diverse fonti con il caso studio riguardante Pezzutti Group Spa, una PMI italiana operante nel settore della gomma/plastica (stampaggio di articoli in materiale plastico) che ha recentemente implementato tecnologie che si rifanno al paradigma della quarta rivoluzione industriale.
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Santos, Luís André Brísio Marques dos. "Implementation and evaluation of a spam classifier based on the dynamic behaviour of immune cells." Dissertação, Porto : [s. n.], 2008. http://catalogo.up.pt/F?func=find-b&local_base=FCB01&find_code=SYS&request=000101268.

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Saindon, Andrée-Anne. "Caractérisation des isoformes de la protéine SPAM1 (Sperm Adhesion Molecule 1) et identification de ses partenaires d'interactions dans les spermatozoïdes." Master's thesis, Université Laval, 2017. http://hdl.handle.net/20.500.11794/27978.

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Sperm Adhesion Molecule 1 (SPAM1) est une protéine spermatique possédant une activité hyaluronidase dans son domaine N-terminal, contribuant à la dispersion des cellules du cumulus entourant l’ovocyte. Elle possède aussi une capacité de liaison à la zone pellucide (ZP) dans son domaine C-terminal. Nos études précédentes chez le taureau ont démontré la présence de deux isoformes potentielles de SPAM1 de ~70 et 80 kDa. Ces mêmes études ont permis d’émettre l’hypothèse que ces deux isoformes de SPAM1 ont des domaines C-terminaux différents, des origines différentes (testicule ou épididyme) et son
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Wolff, Suzanne Christine. "Insulin signaling, dietary restriction and DNA damage multiple roles for smk-1 in the mediation of C. elegans life span /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3311468.

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Thesis (Ph. D.)--University of California, San Diego, 2008.<br>Title from first page of PDF file (viewed July 31, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 157-174).
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Books on the topic "Spam SMS"

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Alain, Morineau. Le modèle log-linéaire et ses applications: La procédure Logli de SPAD. Decisia, 1996.

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Larcher, Éric. L' Internet sécurisé: Comment crypter ses mails, lutter contre le spam et les virus, protéger son anonymat sur le Web. Eyrolles, 2000.

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ʼBrug Sgar Dpe-mdzod-khaṅ (Darjeeling, India), ред. Zla thoʼi tshes khoṅs re la ʼchar baʼi lṅa bsdus gtsoʼi rten ʼbrel ñi ma spar sme sa bdag sogs gsal bar ston paʼi me loṅ bi ha ra tisma. ʼBrug Sgar Dpe-mdzod-khaṅ nas dpar du bskrun, 2004.

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Kountouris, Stefan. Spam ohne Ende?: Unerwünschte Werbung per Email und SMS. Tectum Verlag, 2007.

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Amann, Alexander. Cold Calling, Spam, Werbe-SMS: Combating Windmills on all Fronts. GRIN Verlag GmbH, 2008.

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Rügheimer, Hannes. SMS- und Handy- Trix. Mehr Spaß für weniger Geld. Arena, 2001.

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APEC: Integrated plan of action for SME development (SPAN). Published by Small and Medium Industries Development Corporation (SMIDEC), Malaysia, for Asia-Pacific Economic Cooperation (APEC) Secretariat, Singapore, 1998.

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Charms, Daniil. Einfach Schnickschnack. Spaß für Kinder / Sme nye istorii dlja detej. Dtv, 1995.

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Ramos, Ana Margarita. Memorias fragmentadas en contexto de lucha. Teseo, 2020. http://dx.doi.org/10.55778/ts877232554.

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&lt;p&gt;&lt;span&gt;Este libro reúne investigaciones basadas en&lt;/span&gt;&lt;i&gt;etnografías comprometidas&lt;/i&gt;&lt;span&gt;&lt;span&gt;que se han construido&lt;/span&gt;a partir de interacciones intelectuales, políticas y afectivas entre miembros del&lt;span&gt;&lt;/span&gt;&lt;/span&gt;Grupo de Estudios sobre Memorias Alterizadas y Subordinadas (GEMAS)&lt;span&gt;&lt;span&gt;&lt;/span&gt;y sujetos, familias, comunidades y organizaciones indígenas. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;En contraste con las narrativas totalizantes y homogéneas de la nación –que silencian, banali
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Raffin, Marcelo. Verdad y subjetividad en Michel Foucault (1970-1980). Teseo, 2019. http://dx.doi.org/10.55778/ts877232288.

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&lt;p&gt;&lt;span&gt;Este libro reúne trabajos de investigación que se centran en el análisis de la relación verdad-subjetividad en Michel Foucault entre los años 1970 y 1980. Los estudios toman como punto de partida las líneas que el filósofo va elaborando en el entramado que se puede establecer entre sus libros del periodo (&lt;/span&gt;&lt;i&gt;Vigilar y castigar&lt;/i&gt;&lt;span&gt;&lt;span&gt;&lt;/span&gt;y los tomos de&lt;span&gt;&lt;/span&gt;&lt;/span&gt;&lt;i&gt;Historia de la sexualidad&lt;/i&gt;&lt;span&gt;) y los cursos en el&lt;span&gt;&lt;/span&gt;&lt;/span&gt;&lt;i&gt;Collège de
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Book chapters on the topic "Spam SMS"

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Srinivasa Rao, D., and E. Ajith Jubilson. "SMS Spam Detection Using Federated Learning." In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0609-3_39.

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Sidhpura, Jiten, Parshwa Shah, Rudresh Veerkhare, and Anand Godbole. "FedSpam: Privacy Preserving SMS Spam Prediction." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1645-0_5.

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Saraswathi, D., and D. Sowmya. "SMS Spam Classification Using PSO-C4.5." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7169-3_4.

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Vishwakarma, Arvind Kumar, Mohd Dilshad Ansari, and Gaurav Rai. "SMS Spam Filtering Using Machine Learning Technique." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_66.

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Debnath, Kingshuk, and Nirmalya Kar. "SMS Spam Detection Using Deep Learning Approach." In Human-Centric Smart Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5403-0_29.

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Hossain, Syed Md Minhaz, Anik Sen, and Kaushik Deb. "Detecting Spam SMS Using Self Attention Mechanism." In Intelligent Computing & Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19958-5_17.

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Prasanna Bharathi, P., G. Pavani, K. Krishna Varshitha, and Vaddi Radhesyam. "Spam SMS Filtering Using Support Vector Machines." In Intelligent Data Communication Technologies and Internet of Things. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9509-7_53.

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Basumatary, Baknai, Tenzin Khetsuen Norbu, Sujatha Arun Kokatnoor, and Sandeep Kumar. "Machine Learning Models for SMS Spam Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2647-2_29.

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El Hlouli, Fatima Zohra, Jamal Riffi, Mohamed Adnane Mahraz, Ali El Yahyaouy, and Hamid Tairi. "Detection of SMS Spam Using Machine-Learning Algorithms." In Embedded Systems and Artificial Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6_41.

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Baek, Manki, Youngkyung Lee, and Yoojae Won. "Property Analysis of SMS Spam Using Text Mining." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5041-1_12.

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Conference papers on the topic "Spam SMS"

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Alzubaidi, Ali, and Sakher Ghanem. "Arabic SMS Spam Detection." In 2025 International Conference on Innovation in Artificial Intelligence and Internet of Things (AIIT). IEEE, 2025. https://doi.org/10.1109/aiit63112.2025.11082859.

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Rajput, Satpalsing D., Pratiksha Chopade, Atharva Chivate, Shreeshail Chitpur, and Isha Dashetwar. "Spam SMS Detection Using Natural Language Processing." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774959.

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Mankar, Bhagyashri, Mohini Wanjari, and Diksha Gabhane. "Spam SMS Classifier Using Machine Learning Algorithms." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882288.

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Rajalakshmi, M., R. Rengaraj, M. Karthikeyan, and Aruna S. "NLP-Based SMS Spam Detection Using Ensemble Learning." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10779991.

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Nayak, Amlan, Rina Kumari, Debapam Pal, Sudatta Jana, Aniket Bhardwaj, and Pratim Mangaldas Dasude. "Multilingual SMS Spam Detection using BERT and LSTM." In 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET). IEEE, 2024. http://dx.doi.org/10.1109/icicet59348.2024.10616322.

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Subhashini, G., Mahalakshmi G., H. Mohamed Ashik, and B. Nithin Duresh. "Advanced SMS Spam Detection Using Integrated Feature Extraction." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10867034.

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Arora, Yojna, Neha Gupta, Yogesh Singh Rathore, et al. "SMS Spam Detection using Advance Naive-Bayes Approach." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10940832.

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Sultana, Hafsa, Jamal Uddin Tanvin, Fairooz Tasnia, and Nusrat Sharmin. "Bilingual SMS Spam Detection Using Deep Ensemble Learning." In 2024 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2024. https://doi.org/10.1109/wiecon-ece64149.2024.10915168.

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Nagare, Samadhan M., Pratibha P. Dapke, Syed Ahteshamuddin Quadri, Sagar B. Bandal, Ramnath M. Gaikwad, and Manasi R. Baheti. "Pre-Processing Techniques for Mobile SMS Spam Detection." In 2024 IEEE Pune Section International Conference (PuneCon). IEEE, 2024. https://doi.org/10.1109/punecon63413.2024.10895745.

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Shdefat, Ahmed Younes, Nouran M. Sedky, Zeina H. El Bialy, Shahd Ahmed, Hanaa Fathi, and Diaa Salama AbdElminaam. "Machine Learning-Based Solution for SMS Spam Detection Problem." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652878.

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Reports on the topic "Spam SMS"

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Gayen, Saumabha, Enrique D. Saldivar-Carranza, Rahul Suryakant Sakhare, et al. Statewide Screening of Signalized Intersections for Capacity Improvements. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317755.

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Identification of congested traffic signals that require capital investment to increase capacity has historically been a time-consuming process. Signalized intersections with congestion were analyzed to see if they could be improved through retiming, and capital investment was only considered if retiming is deemed infeasible. Automated Traffic Signal Performance Measures (ATSPMs), and more recently, signal performance measures (SPMs) derived from connected vehicle (CV) trajectory data, have already been used to streamline the process of identifying signalized intersections that can be improved
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2

Ayers, Dotson, and Alexander. L52332 Offshore Pipeline Damage Emergency Response Guidelines. Pipeline Research Council International, Inc. (PRCI), 2012. http://dx.doi.org/10.55274/r0010016.

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Subsea pipelines and flow lines are periodically subjected to damaging events such as anchor impacts that result in massive pipeline movements, dropped object damage, internal/external corrosion damage, etc. Knowing how to assess these damage events is often challenging, especially considering the potential for product release. The cost of production shut-ins can be significant and avoiding un-necessary shut-ins is desirable. While most pipeline operators have company-level procedures and programs in place for responding to pipeline emergencies, at the current time there is no single resource
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