Farkhund Iqbal
- Ahmed Abbasi, Abdul Rehman Javed, Farkhund Iqbal, Zunera Jalil, Thippa Reddy Gadekallu, and Natalia Kryvinska. 2022. Authorship identification using ensemble learning. Scientific Reports 12, 1 (2022), 1–16.Google Scholar
Cross Ref
- Ahmed Abbasi, Abdul Rehman Rehman Javed, Amanullah Yasin, Zunera Jalil, Natalia Kryvinska, and Usman Tariq. 2022. A Large-Scale Benchmark Dataset for Anomaly Detection and Rare Event Classification for Audio Forensics. IEEE Access 10(2022), 38885–38894.Google ScholarCross Ref
- Nakul Agarwal, Vineeth N Balasubramanian, and CV Jawahar. 2018. Improving multiclass classification by deep networks using dagsvm and triplet loss. Pattern Recognition Letters 112 (2018), 184–190.Google ScholarCross Ref
- Adnan Ahmed, Abdul Rehman Javed, Zunera Jalil, Gautam Srivastava, and Thippa Reddy Gadekallu. 2021. Privacy of web browsers: a challenge in digital forensics. In International Conference on Genetic and Evolutionary Computing. Springer, 493–504.Google Scholar
- Naeem Ahmed, Rashid Amin, Hamza Aldabbas, Deepika Koundal, Bader Alouffi, and Tariq Shah. 2022. Machine learning techniques for spam detection in email and IoT platforms: analysis and research challenges. Security and Communication Networks 2022 (2022), 1–19.Google ScholarDigital Library
- Usman Ahmed, Rutvij H Jhaveri, Gautam Srivastava, and Jerry Chun-Wei Lin. 2022. Explainable deep attention active learning for sentimental analytics of mental disorder. Transactions on Asian and Low-Resource Language Information Processing (2022).Google Scholar
- Waqas Ahmed, Faisal Shahzad, Abdul Rehman Javed, Farkhund Iqbal, and Liaqat Ali. 2021. Whatsapp network forensics: Discovering the ip addresses of suspects. In 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 1–7.Google ScholarCross Ref
- Yusra Al Balushi, Hothefa Shaker, and Basant Kumar. 2023. The Use of Machine Learning in Digital Forensics. In 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022). Atlantis Press, 96–113.Google Scholar
- AM Al-Zoubi and H Faris. 2018. J. f. Alqatawna, and MA Hassonah,“Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts,”. Knowledge-Based Systems 153 (2018), 91–104.Google ScholarDigital Library
- Abdullah Alqahtani, Habib Ullah Khan, Shtwai Alsubai, Mohemmed Sha, Ahmad Almadhor, Tayyab Iqbal, and Sidra Abbas. 2022. An efficient approach for textual data classification using deep learning. (2022).Google Scholar
- I Basyar, Murdiansyah DT Adiwijaya, and DT Murdiansyah. 2020. Email spam classification using gated recurrent unit and long short-term memory. Journal of Computer Science 16, 4 (2020), 559–567.Google ScholarCross Ref
- Gourav Bathla and Adarsh Kumar. 2021. Opinion spam detection using Deep Learning. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 1160–1164.Google ScholarCross Ref
- Asma Belhadi, Youcef Djenouri, Gautam Srivastava, and Jerry Chun-Wei Lin. 2023. Fast and Accurate Framework for Ontology Matching in Web of Things. ACM Transactions on Asian and Low-Resource Language Information Processing (2023).Google Scholar
- Diego Benito, Oscar Araque, and Carlos A. Iglesias. 2019. GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation. Association for Computational Linguistics, Minneapolis, Minnesota, USA, 396–403. https://doi.org/10.18653/v1/S19-2070Google ScholarCross Ref
- Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics 5 (2017), 135–146. https://doi.org/10.1162/tacl_a_00051Google ScholarCross Ref
- Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5–32.Google ScholarDigital Library
- Jie Cao and Chengzhe Lai. 2020. A bilingual multi-type spam detection model based on M-BERT. In GLOBECOM 2020-2020 IEEE Global Communications Conference. IEEE, 1–6.Google ScholarCross Ref
- Gurpal Singh Chhabra and Dilpreet Singh Bajwa. 2015. Review of e-mail system, security protocols and email forensics. International Journal of Computer Science & Communication Networks 5, 3(2015), 201–211.Google Scholar
- Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 8440–8451. https://doi.org/10.18653/v1/2020.acl-main.747Google ScholarCross Ref
- Michael Crawford and Taghi M Khoshgoftaar. 2021. Using inductive transfer learning to improve hotel review spam detection. In 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 248–254.Google ScholarDigital Library
- Giovanni Da San Martino, Shaden Shaar, Yifan Zhang, Seunghak Yu, Alberto Barrón-Cedeño, and Preslav Nakov. 2020. Prta: A System to Support the Analysis of Propaganda Techniques in the News. arXiv (2020), arXiv–2005.Google Scholar
- S Jancy Sickory Daisy and A Rijuvana Begum. 2021. Smart material to build mail spam filtering technique using Naive Bayes and MRF methodologies. Materials Today: Proceedings 47 (2021), 446–452.Google ScholarCross Ref
- Bilge Kagan Dedeturk and Bahriye Akay. 2020. Spam filtering using a logistic regression model trained by an artificial bee colony algorithm. Applied Soft Computing 91 (2020), 106229.Google ScholarCross Ref
- Ahmet Demirkaya, Jiasi Chen, and Samet Oymak. 2020. Exploring the role of loss functions in multiclass classification. In 2020 54th Annual Conference on Information Sciences and Systems (CISS). IEEE, 1–5.Google ScholarCross Ref
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. https://doi.org/10.18653/v1/N19-1423Google Scholar
- M Muztaba Fuad, Debzani Deb, and M Shahriar Hossain. 2004. A trainable fuzzy spam detection system. In Proc. of the 7th Int. Conf. on Computer and Information Technology.Google Scholar
- Surajit Giri, Siddhartha Banerjee, Kunal Bag, and Dipanjan Maiti. 2022. Comparative Study of Content-Based Phishing Email Detection Using Global Vector (GloVe) and Bidirectional Encoder Representation from Transformer (BERT) Word Embedding Models. In 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 01–06.Google ScholarCross Ref
- Bahia Halawi, Azzam Mourad, Hadi Otrok, and Ernesto Damiani. 2018. Few are as good as many: An ontology-based tweet spam detection approach. IEEE Access 6(2018), 63890–63904.Google ScholarCross Ref
- Maryam Hina, Mohsin Ali, Abdul Rehman Javed, Fahad Ghabban, Liaqat Ali Khan, and Zunera Jalil. 2021. Sefaced: Semantic-based forensic analysis and classification of e-mail data using deep learning. IEEE Access 9(2021), 98398–98411.Google ScholarCross Ref
- Maryam Hina, Mohsan Ali, Abdul Rehman Javed, Gautam Srivastava, Thippa Reddy Gadekallu, and Zunera Jalil. 2021. Email Classification and Forensics Analysis using Machine Learning. In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI). IEEE, 630–635.Google Scholar
- Maryam Hina, Mohsan Ali, Abdul Rehman Javed, Gautam Srivastava, Thippa Reddy Gadekallu, and Zunera Jalil. 2021. Email Classification and Forensics Analysis using Machine Learning. In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI). IEEE, 630–635.Google Scholar
- Yaoshiang Ho and Samuel Wookey. 2019. The real-world-weight cross-entropy loss function: Modeling the costs of mislabeling. IEEE Access 8(2019), 4806–4813.Google ScholarCross Ref
- Gauri Jain, Manisha Sharma, and Basant Agarwal. 2019. Spam detection in social media using convolutional and long short term memory neural network. Annals of Mathematics and Artificial Intelligence 85, 1(2019), 21–44.Google ScholarDigital Library
- Abdul Rehman Javed, Waqas Ahmed, Mamoun Alazab, Zunera Jalil, Kashif Kifayat, and Thippa Reddy Gadekallu. 2022. A comprehensive survey on computer forensics: State-of-the-art, tools, techniques, challenges, and future directions. IEEE Access (2022).Google Scholar
- Abdul Rehman Javed and Zunera Jalil. 2020. Byte-level object identification for forensic investigation of digital images. In 2020 International Conference on Cyber Warfare and Security (ICCWS). IEEE, 1–4.Google ScholarCross Ref
- Abdul Rehman Javed, Zunera Jalil, Wisha Zehra, Thippa Reddy Gadekallu, Doug Young Suh, and Md Jalil Piran. 2021. A comprehensive survey on digital video forensics: Taxonomy, challenges, and future directions. Engineering Applications of Artificial Intelligence 106 (2021), 104456.Google ScholarDigital Library
- Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. Bag of Tricks for Efficient Text Classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, Valencia, Spain, 427–431. https://www.aclweb.org/anthology/E17-2068Google ScholarCross Ref
- Akash Junnarkar, Siddhant Adhikari, Jainam Fagania, Priya Chimurkar, and Deepak Karia. 2021. E-mail spam classification via machine learning and natural language processing. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 693–699.Google ScholarCross Ref
- Sanaa Kaddoura, Ganesh Chandrasekaran, Daniela Elena Popescu, and Jude Hemanth Duraisamy. 2022. A systematic literature review on spam content detection and classification. PeerJ Computer Science 8(2022), e830.Google ScholarCross Ref
- Hannah Kim and Young-Seob Jeong. 2019. Sentiment classification using convolutional neural networks. Applied Sciences 9, 11 (2019), 2347.Google ScholarCross Ref
- Piotr Kłosowski. 2018. Deep learning for natural language processing and language modelling. In 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 223–228.Google Scholar
- Priti Kulkarni, Jatinderkumar R Saini, and Haridas Acharya. 2020. Effect of header-based features on accuracy of classifiers for spam email classification. International Journal of Advanced Computer Science and Applications 11, 3(2020).Google ScholarCross Ref
- Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Recurrent convolutional neural networks for text classification. In Twenty-ninth AAAI conference on artificial intelligence.Google ScholarDigital Library
- Yuxin Liu, Li Wang, Tengfei Shi, and Jinyan Li. 2022. Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM. Information Systems 103(2022), 101865.Google ScholarDigital Library
- Qin Luo, Bin Liu, Junhua Yan, and Zhongyue He. 2011. Design and implement a rule-based spam filtering system using neural network. In 2011 International Conference on Computational and Information Sciences. IEEE, 398–401.Google ScholarDigital Library
- Arun S Maiya. 2020. ktrain: A low-code library for augmented machine learning. arXiv preprint arXiv:2004.10703(2020).Google Scholar
- RAZA Mansoor, Nathali Dilshani Jayasinghe, and Muhana Magboul Ali Muslam. 2021. A comprehensive review on email spam classification using machine learning algorithms. In 2021 International Conference on Information Networking (ICOIN). IEEE, 327–332.Google Scholar
- Weilong Mo, Xiaoshu Luo, Yexiu Zhong, and Wenjie Jiang. 2019. Image recognition using convolutional neural network combined with ensemble learning algorithm. In Journal of Physics: Conference Series, Vol. 1237. IOP Publishing, 022026.Google Scholar
- Kamran Morovati and Sanjay S Kadam. 2019. Detection of Phishing Emails with Email Forensic Analysis and Machine Learning Techniques.International Journal of Cyber-Security and Digital Forensics 8, 2(2019), 98–108.Google Scholar
- Rakesh Nayak, Salim Amirali Jiwani, and B Rajitha. 2021. Spam email detection using machine learning algorithm. Materials Today: Proceedings(2021).Google ScholarCross Ref
- Ahmad Nsouli, Azzam Mourad, and Danielle Azar. 2018. Towards proactive social learning approach for traffic event detection based on arabic tweets. In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE, 1501–1506.Google ScholarCross Ref
- Khan Farhan Rafat, Qin Xin, Abdul Rehman Javed, Zunera Jalil, and Rana Zeeshan Ahmad. 2022. Evading obscure communication from spam emails. Mathematical Biosciences and Engineering 19, 2 (2022), 1926–1943.Google ScholarCross Ref
- Nadjate Saidani, Kamel Adi, and Mohand Said Allili. 2020. A semantic-based classification approach for an enhanced spam detection. Computers & Security 94(2020), 101716.Google ScholarCross Ref
- Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108(2019).Google Scholar
- Robert E. Schapire. 1999. A Brief Introduction to Boosting. In Proceedings of the 16th International Joint Conference on Artificial Intelligence - Volume 2 (IJCAI’99). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1401–1406.Google ScholarDigital Library
- Alexander Semenov, Vladimir Boginski, and Eduardo L Pasiliao. 2019. Neural Networks with Multidimensional Cross-Entropy Loss Functions. In International Conference on Computational Data and Social Networks. Springer, 57–62.Google Scholar
- Neha Sharma, Vibhor Jain, and Anju Mishra. 2018. An analysis of convolutional neural networks for image classification. Procedia computer science 132 (2018), 377–384.Google Scholar
- Vishnu Dutt Sharma, Santosh Kumar Yadav, Sumit Kumar Yadav, Kamakhya Narain Singh, and Suraj Sharma. 2021. An effective approach to protect social media account from spam mail–a machine learning approach. Materials Today: Proceedings(2021).Google ScholarCross Ref
- Jitendra Nath Shrivastava and Maringanti Hima Bindu. 2014. E-mail spam filtering using adaptive genetic algorithm. International Journal of Intelligent Systems and Applications 6, 2(2014), 54–60.Google ScholarCross Ref
- Amar Singh, Nidhi Chahal, Simranjit Singh, and Suneet Kumar Gupta. 2021. Spam Detection using ANN and ABC Algorithm. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 164–168.Google ScholarCross Ref
- Peng Song, Chaoyang Geng, and Zhijie Li. 2019. Research on text classification based on convolutional neural network. In 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA). IEEE, 229–232.Google ScholarCross Ref
- Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075(2015).Google Scholar
- Xiaoya Tang, Tieyun Qian, and Zhenni You. 2020. Generating behavior features for cold-start spam review detection with adversarial learning. Information Sciences 526(2020), 274–288.Google ScholarCross Ref
- Xin Tong, Jingya Wang, Changlin Zhang, Runzheng Wang, Zhilin Ge, Wenmao Liu, and Zhiyan Zhao. 2021. A content-based chinese spam detection method using a capsule network with long-short attention. IEEE Sensors Journal 21, 22 (2021), 25409–25420.Google ScholarCross Ref
- Amirsina Torfi, Rouzbeh A Shirvani, Yaser Keneshloo, Nader Tavaf, and Edward A Fox. 2020. Natural language processing advancements by deep learning: A survey. arXiv preprint arXiv:2003.01200(2020).Google Scholar
- Marco Vanetti, Elisabetta Binaghi, Elena Ferrari, Barbara Carminati, and Moreno Carullo. 2011. A system to filter unwanted messages from OSN user walls. IEEE Transactions on Knowledge and data Engineering 25, 2(2011), 285–297.Google ScholarDigital Library
- S Venkatraman, B Surendiran, and P Arun Raj Kumar. 2020. Spam e-mail classification for the Internet of Things environment using semantic similarity approach. The Journal of Supercomputing 76, 2 (2020), 756–776.Google ScholarDigital Library
- Sandeep Verma, Sherali Zeadally, Satnam Kaur, and Ajay Kumar Sharma. 2021. Intelligent and Secure Clustering in Wireless Sensor Network (WSN)-Based Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems (2021).Google Scholar
- Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38–45. https://doi.org/10.18653/v1/2020.emnlp-demos.6Google ScholarCross Ref
- Chih-Hung Wu. 2009. Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert systems with Applications 36, 3 (2009), 4321–4330.Google Scholar
- Qussai Yaseen et al. 2021. Spam email detection using deep learning techniques. Procedia Computer Science 184 (2021), 853–858.Google ScholarCross Ref
- Ammara Zamir, Hikmat Ullah Khan, Waqar Mehmood, Tassawar Iqbal, and Abubakker Usman Akram. 2020. A feature-centric spam email detection model using diverse supervised machine learning algorithms. The Electronic Library(2020).Google ScholarCross Ref
- Yangfan Zhou, Xin Wang, Mingchuan Zhang, Junlong Zhu, Ruijuan Zheng, and Qingtao Wu. 2019. MPCE: a maximum probability based cross entropy loss function for neural network classification. IEEE Access 7(2019), 146331–146341.Google ScholarCross Ref
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