{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:27:14Z","timestamp":1774434434389,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s40747-026-02247-x","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T04:18:08Z","timestamp":1771906688000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient multilingual spam detection on resource-constrained devices: a comparative analysis of QLoRA fine-tuning of Gemma 3, Qwen 3, and Llama 3.2 models"],"prefix":"10.1007","volume":"12","author":[{"given":"Hamza","family":"Rauf","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umair","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jomana A.","family":"Bashatah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aurang","family":"Zaib","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"issue":"7","key":"2247_CR1","doi-asserted-by":"publisher","first-page":"3634","DOI":"10.1016\/j.eswa.2014.12.029","volume":"42","author":"A Heydari","year":"2015","unstructured":"Heydari A, ali Tavakoli M, Salim N, Heydari Z (2015) Detection of review spam: a survey. Expert Syst Appl 42(7):3634\u20133642","journal-title":"Expert Syst Appl"},{"issue":"10","key":"2247_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/fi12100168","volume":"12","author":"R Alabdan","year":"2020","unstructured":"Alabdan R (2020) Phishing attacks survey: types, vectors, and technical approaches. Future Internet 12(10):168","journal-title":"Future Internet"},{"key":"2247_CR3","doi-asserted-by":"crossref","unstructured":"Garg P, Girdhar N (2021) A systematic review on spam filtering techniques based on natural language processing framework. In: 2021 11th international conference on cloud computing, data science & engineering (confluence). IEEE, pp 30\u201335","DOI":"10.1109\/Confluence51648.2021.9377042"},{"key":"2247_CR4","doi-asserted-by":"crossref","unstructured":"Dada EG, Bassi JS, Chiroma H, Abdulhamid SIM, Adetunmbi AO, Ajibuwa OE (2019) Machine learning for email spam filtering: review, approaches and open research problems. Heliyon 5(6)","DOI":"10.1016\/j.heliyon.2019.e01802"},{"key":"2247_CR5","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ijhcs.2018.12.004","volume":"125","author":"A Ferreira","year":"2019","unstructured":"Ferreira A, Teles S (2019) Persuasion: how phishing emails can influence users and bypass security measures. Int J Hum Comput Stud 125:19\u201331","journal-title":"Int J Hum Comput Stud"},{"key":"2247_CR6","unstructured":"Paswan MK, Bala PS, Aghila G (2012) Spam filtering: comparative analysis of filtering techniques. In: IEEE-international conference on advances in engineering, science and management (ICAESM-2012). IEEE, pp 170\u2013176"},{"key":"2247_CR7","doi-asserted-by":"publisher","first-page":"82653","DOI":"10.1109\/ACCESS.2020.2991328","volume":"8","author":"T Xia","year":"2020","unstructured":"Xia T (2020) A constant time complexity spam detection algorithm for boosting throughput on rule-based filtering systems. IEEE Access 8:82653\u201382661","journal-title":"IEEE Access"},{"issue":"1","key":"2247_CR8","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1575\/1\/012054","volume":"1575","author":"T Lv","year":"2020","unstructured":"Lv T, Yan P, Yuan H, He W (2020) Spam filter based on naive Bayesian classifier. J Phys Conf Ser 1575(1):012054","journal-title":"J Phys Conf Ser"},{"key":"2247_CR9","doi-asserted-by":"publisher","first-page":"2856","DOI":"10.1109\/TIFS.2023.3255172","volume":"18","author":"I Kim","year":"2023","unstructured":"Kim I, Susilo W, Baek J, Kim J, Chow YW (2023) PCSF: privacy-preserving content-based spam filter. IEEE Trans Inf Forensics Secur 18:2856\u20132869","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2247_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.108826","volume":"206","author":"S Magdy","year":"2022","unstructured":"Magdy S, Abouelseoud Y, Mikhail M (2022) Efficient spam and phishing emails filtering based on deep learning. Comput Netw 206:108826","journal-title":"Comput Netw"},{"issue":"4","key":"2247_CR11","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1049\/cje.2021.05.001","volume":"30","author":"GU Zhaoquan","year":"2021","unstructured":"Zhaoquan GU, Yushun XIE, Weixiong HU, Lihua YIN, Yi HAN, Zhihong TIAN (2021) Marginal attacks of generating adversarial examples for spam filtering. Chin J Electron 30(4):595\u2013602","journal-title":"Chin J Electron"},{"key":"2247_CR12","doi-asserted-by":"crossref","unstructured":"\u015eahin D\u00d6, Demirci S (2020) Spam filtering with KNN: Investigation of the effect of k value on classification performance. In: 2020 28th signal processing and communications applications conference (SIU). IEEE, pp 1\u20134","DOI":"10.1109\/SIU49456.2020.9302516"},{"issue":"17","key":"2247_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10172083","volume":"10","author":"S Rapacz","year":"2021","unstructured":"Rapacz S, Cho\u0142da P, Natkaniec M (2021) A method for fast selection of machine-learning classifiers for spam filtering. Electronics 10(17):2083","journal-title":"Electronics"},{"key":"2247_CR14","doi-asserted-by":"crossref","unstructured":"Chandan JR, Dsouza GE, George M, Bhadra J (2022) Spam message filtering based on machine learning algorithms and BERT. In: Intelligent communication technologies and virtual mobile networks: proceedings of ICICV 2022. Springer Nature Singapore, Singapore, pp 227\u2013238","DOI":"10.1007\/978-981-19-1844-5_19"},{"issue":"13","key":"2247_CR15","doi-asserted-by":"publisher","first-page":"9625","DOI":"10.1007\/s00500-019-04473-7","volume":"24","author":"D Gaurav","year":"2020","unstructured":"Gaurav D, Tiwari SM, Goyal A, Gandhi N, Abraham A (2020) Machine intelligence-based algorithms for spam filtering on document labeling. Soft Comput 24(13):9625\u20139638","journal-title":"Soft Comput"},{"key":"2247_CR16","doi-asserted-by":"crossref","unstructured":"Mehrotra T, Rajput GK, Verma M, Lakhani B, Singh N (2021) Email spam filtering technique from various perspectives using machine learning algorithms. In: Data driven approach towards disruptive technologies: proceedings of MIDAS 2020. Springer Singapore, Singapore, pp 423\u2013432","DOI":"10.1007\/978-981-15-9873-9_33"},{"key":"2247_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110478","volume":"144","author":"G Manita","year":"2023","unstructured":"Manita G, Chhabra A, Korbaa O (2023) Efficient e-mail spam filtering approach combining logistic regression model and orthogonal atomic orbital search algorithm. Appl Soft Comput 144:110478","journal-title":"Appl Soft Comput"},{"key":"2247_CR18","doi-asserted-by":"crossref","unstructured":"Kuchipudi B, Nannapaneni RT, Liao Q (2020) Adversarial machine learning for spam filters. In: Proceedings of the 15th international conference on availability, reliability and security, pp 1\u20136","DOI":"10.1145\/3407023.3407079"},{"key":"2247_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102038","volume":"102","author":"K Zhang","year":"2024","unstructured":"Zhang K, Zhou F, Wu L, Xie N, He Z (2024) Semantic understanding and prompt engineering for large-scale traffic data imputation. Inf Fusion 102:102038","journal-title":"Inf Fusion"},{"key":"2247_CR20","doi-asserted-by":"crossref","unstructured":"Gomaa WH (2020) The impact of deep learning techniques on SMS spam filtering. Int J Adv Comput Sci Appl 11(1)","DOI":"10.14569\/IJACSA.2020.0110167"},{"key":"2247_CR21","doi-asserted-by":"crossref","unstructured":"Cao J, Lai C (2020) A bilingual multi-type spam detection model based on M-BERT. In: GLOBECOM 2020\u20132020 IEEE global communications conference. IEEE, pp 1\u20136","DOI":"10.1109\/GLOBECOM42002.2020.9347970"},{"key":"2247_CR22","doi-asserted-by":"crossref","unstructured":"Baaqeel H, Zagrouba R (2020) Hybrid SMS spam filtering system using machine learning techniques. In: 2020 21st international arab conference on information technology (ACIT). IEEE, pp 1\u20138","DOI":"10.1109\/ACIT50332.2020.9300071"},{"key":"2247_CR23","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.procs.2021.06.056","volume":"190","author":"Y Kontsewaya","year":"2021","unstructured":"Kontsewaya Y, Antonov E, Artamonov A (2021) Evaluating the effectiveness of machine learning methods for spam detection. Proc Comput Sci 190:479\u2013486","journal-title":"Proc Comput Sci"},{"key":"2247_CR24","doi-asserted-by":"crossref","unstructured":"Hnini G, Riffi J, Mahraz MA, Yahyaouy A, Tairi H (2020) Spam filtering system based on nearest neighbor algorithms. In: International conference on artificial intelligence & industrial applications. Springer International Publishing, Cham, pp 36\u201346","DOI":"10.1007\/978-3-030-53970-2_4"},{"issue":"26","key":"2247_CR25","doi-asserted-by":"publisher","first-page":"40819","DOI":"10.1007\/s11042-023-15170-x","volume":"82","author":"M Kihal","year":"2023","unstructured":"Kihal M, Hamza L (2023) Robust multimedia spam filtering based on visual, textual, and audio deep features and random forest. Multimed Tools Appl 82(26):40819\u201340837","journal-title":"Multimed Tools Appl"},{"key":"2247_CR26","doi-asserted-by":"crossref","unstructured":"Shaik CM, Penumaka NM, Abbireddy SK, Kumar V, Aravinth SS (2023) Bi-lstm and conventional classifiers for email spam filtering. In: 2023 third international conference on artificial intelligence and smart energy (ICAIS). IEEE, pp 1350\u20131355","DOI":"10.1109\/ICAIS56108.2023.10073776"},{"key":"2247_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.107959","volume":"165","author":"M Ghiassi","year":"2022","unstructured":"Ghiassi M, Lee S, Gaikwad SR (2022) Sentiment analysis and spam filtering using the YAC2 clustering algorithm with transferability. Comput Ind Eng 165:107959","journal-title":"Comput Ind Eng"},{"issue":"1","key":"2247_CR28","volume":"2021","author":"C Wang","year":"2021","unstructured":"Wang C, Li Q, Ren TY, Wang XH, Guo GX (2021) High efficiency spam filtering: a manifold learning\u2010based approach. Math Probl Eng 2021(1):2993877","journal-title":"Math Probl Eng"},{"issue":"1","key":"2247_CR29","first-page":"40","volume":"12","author":"P Bhattacharya","year":"2020","unstructured":"Bhattacharya P, Singh A (2020) E-mail spam filtering using genetic algorithm based on probabilistic weights and words count. Int J Integr Eng 12(1):40\u201349","journal-title":"Int J Integr Eng"},{"issue":"1","key":"2247_CR30","volume":"2022","author":"N Ahmed","year":"2022","unstructured":"Ahmed N, Amin R, Aldabbas H, Koundal D, Alouffi B, Shah T (2022) Machine learning techniques for spam detection in email and IoT platforms: analysis and research challenges. Secur Commun Netw 2022(1):1862888","journal-title":"Secur Commun Netw"},{"key":"2247_CR31","doi-asserted-by":"crossref","unstructured":"Bhopale AP, Tiwari A (2021) An application of transfer learning: fine-tuning BERT for spam email classification. In: International conference on machine learning and big data analytics. Springer International Publishing, Cham, pp 67\u201377","DOI":"10.1007\/978-3-030-82469-3_6"},{"issue":"1","key":"2247_CR32","doi-asserted-by":"publisher","first-page":"9","DOI":"10.11591\/ijict.v9i1.pp9-18","volume":"9","author":"AA Ojugo","year":"2020","unstructured":"Ojugo AA, Eboka AO (2020) Memetic algorithm for short messaging service spam filter using text normalization and semantic approach. Int J Inform Commun Technol (IJ-ICT) 9(1):9","journal-title":"Int J Inform Commun Technol (IJ-ICT)"},{"key":"2247_CR33","unstructured":"Topal MO, Bas A, van Heerden I (2021) Exploring transformers in natural language generation: Gpt, bert, and xlnet. arXiv:2102.08036"},{"key":"2247_CR34","doi-asserted-by":"crossref","unstructured":"Ji K, Kwon Y (2023) New spam filtering method with Hadoop tuning-based MapReduce Naive Bayes. Comput Syst Sci Eng 45(1)","DOI":"10.32604\/csse.2023.031270"},{"key":"2247_CR35","doi-asserted-by":"crossref","unstructured":"Rifat N, Ahsan M, Chowdhury M, Gomes R (2022) Bert against social engineering attack: phishing text detection. In: 2022 IEEE international conference on electro information technology (eIT). IEEE, pp 1\u20136","DOI":"10.1109\/eIT53891.2022.9813922"},{"key":"2247_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.nlp.2024.100056","volume":"6","author":"KI Roumeliotis","year":"2024","unstructured":"Roumeliotis KI, Tselikas ND, Nasiopoulos DK (2024) LLMs in e-commerce: a comparative analysis of GPT and LLaMA models in product review evaluation. Nat Lang Process J 6:100056","journal-title":"Nat Lang Process J"},{"issue":"1","key":"2247_CR37","doi-asserted-by":"publisher","first-page":"62","DOI":"10.3390\/software3010004","volume":"3","author":"KI Roumeliotis","year":"2024","unstructured":"Roumeliotis KI, Tselikas ND, Nasiopoulos DK (2024) Precision-driven product recommendation software: unsupervised models, evaluated by GPT-4 LLM for enhanced recommender systems. Software 3(1):62\u201380","journal-title":"Software"},{"issue":"13","key":"2247_CR38","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11132053","volume":"11","author":"SG Nam","year":"2022","unstructured":"Nam SG, Jang Y, Lee DG, Seo YS (2022) Hybrid features by combining visual and text information to improve spam filtering performance. Electronics 11(13):2053","journal-title":"Electronics"},{"key":"2247_CR39","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"2247_CR40","unstructured":"ALBERT. https:\/\/huggingface.co\/docs\/transformers\/model_doc\/albert"},{"key":"2247_CR41","doi-asserted-by":"publisher","first-page":"10088","DOI":"10.52202\/075280-0441","volume":"36","author":"T Dettmers","year":"2023","unstructured":"Dettmers T, Pagnoni A, Holtzman A, Zettlemoyer L (2023) Qlora: Efficient finetuning of quantized llms. Adv Neural Inf Process Syst 36:10088\u201310115","journal-title":"Adv Neural Inf Process Syst"},{"key":"2247_CR42","unstructured":"Team G, Kamath A, Ferret J, Pathak S, Vieillard N, Merhej R, Pouget-Abadie J (2025) Gemma 3 technical report. arXiv:2503.19786"},{"key":"2247_CR43","unstructured":"Yang A, Li A, Yang B, Zhang B, Hui B, Zheng B, Qiu Z (2025) Qwen 3 technical report. arXiv:2505.09388."},{"key":"2247_CR44","unstructured":"Dubey A, Jauhri A, Pandey A, Kadian A, Al-Dahle A, Letman A, Ganapathy R (2024) The llama 3 herd of models. arXiv:2407.21783"},{"key":"2247_CR45","doi-asserted-by":"crossref","unstructured":"Warner B, Chaffin A, Clavi\u00e9 B, Weller O, Hallstr\u00f6m O, Taghadouini S, Poli I (2024). Smarter, better, faster, longer: a modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference. arXiv:2412.13663","DOI":"10.18653\/v1\/2025.acl-long.127"},{"key":"2247_CR46","doi-asserted-by":"crossref","unstructured":"\u00dcst\u00fcn A, Aryabumi V, Yong ZX, Ko WY, D'souza D, Onilude G, Hooker S (2024) Aya model: an instruction finetuned open-access multilingual language model. arXiv:2402.07827","DOI":"10.18653\/v1\/2024.acl-long.845"},{"issue":"11","key":"2247_CR47","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13112034","volume":"13","author":"KI Roumeliotis","year":"2024","unstructured":"Roumeliotis KI, Tselikas ND, Nasiopoulos DK (2024) Next-generation spam filtering: comparative fine-tuning of LLMs, NLPs, and CNN models for email spam classification. Electronics 13(11):2034. https:\/\/doi.org\/10.3390\/electronics13112034","journal-title":"Electronics"},{"key":"2247_CR48","unstructured":"Meta AI (2024) Llama 3.2 Connect 2024: vision, edge, and mobile devices. Meta AI Blog. https:\/\/ai.meta.com\/blog\/llama-3-2-connect-2024-vision-edge-mobile-devices. Accessed 10 June 2025"},{"key":"2247_CR49","doi-asserted-by":"publisher","unstructured":"Enhancing IoT security with AI-driven hybrid machine learning and neural network-based intrusion detection system (T. Nay, Trans.) (2024) Babylon J Artif Intell 2024:158\u2013167. https:\/\/doi.org\/10.58496\/BJAI\/2024\/017","DOI":"10.58496\/BJAI\/2024\/017"},{"key":"2247_CR50","doi-asserted-by":"publisher","first-page":"81","DOI":"10.70470\/SHIFRA\/2025\/005","volume":"2025","author":"A Ali","year":"2025","unstructured":"Ali A, Ghanem MC (2025) Beyond detection: large language models and next-generation cybersecurity. SHIFRA 2025:81\u201397. https:\/\/doi.org\/10.70470\/SHIFRA\/2025\/005","journal-title":"SHIFRA"},{"key":"2247_CR51","doi-asserted-by":"publisher","unstructured":"Does lack of knowledge and hardship of information access signify powerful AI? A large language model perspective (Idrees A. Zahid & Shahad Sabbar Joudar, Trans.). (2023). Appl Data Sci Anal 2023:150\u2013154. https:\/\/doi.org\/10.58496\/ADSA\/2023\/014","DOI":"10.58496\/ADSA\/2023\/014"},{"key":"2247_CR52","doi-asserted-by":"publisher","unstructured":"Mohammed SY, Aljanabi M (2024) Advancing translation quality assessment: integrating AI models for real-time feedback. EDRAAK 2024:1\u20137. https:\/\/doi.org\/10.70470\/EDRAAK\/2024\/001","DOI":"10.70470\/EDRAAK\/2024\/001"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-026-02247-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-026-02247-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-026-02247-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:55:28Z","timestamp":1774425328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-026-02247-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["2247"],"URL":"https:\/\/doi.org\/10.1007\/s40747-026-02247-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,24]]},"assertion":[{"value":"4 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"119"}}