SMS Spam Classification Using Machine Learning
SMS Spam Classification Using Machine Learning

Author: Mandar Shivaji Hanchate

Publisher:

Published: 2023

Total Pages: 0

ISBN-13:

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In recent times, Email and text messages are widely used to communicate as the number of cell phones/mobiles has increased drastically. Short Message Service (SMS) is one of the best and fast ways to communicate. SMSs are used and sent globally for personal and business purposes. But along with important SMSs, we receive other unimportant and fraudulent SMSs too, which is very inconvenient to the users. A lot of bogus messages are being sent for both personal and professional reasons, which is contributing to the problem of SMS spam. Accurately identifying spam SMS is a difficult and important endeavor and the detection of spam is seen as a serious issue in text analysis. The objective of this research is to build a model utilizing machine learning and deep learning principles so that we can understand the semantics of text and then categorize the SMSs as precisely as possible in the spam or non-spam/ham/legitimate classes. Here we used a pre-trained BERT model and collaborated it with several machine learning and deep learning model, among these models, BERT+SVC and BERT+BiLSTM performed the best with 99.10% and 99.19% accuracy respectively on the test dataset.

Email Spam Filtering
Email Spam Filtering

Author: Gordon V. Cormack

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 136

ISBN-13: 1601981465

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Email Spam Filtering: A Systematic Review surveys current and proposed spam filtering techniques with particular emphasis on how well they work. The primary focus is on spam filtering in email, while similarities and differences with spam filtering in other communication and storage media - such as instant messaging and the Web - are addressed peripherally. Email Spam Filtering: A Systematic Review examines the definition of spam, the user's information requirements and the role of the spam filter as one component of a large and complex information universe. Well known methods are detailed sufficiently to make the exposition self-contained; however, the focus is on considerations unique to spam. Comparisons, wherever possible, use common evaluation measures and control for differences in experimental setup. Such comparisons are not easy, as benchmarks, measures and methods for evaluating spam filters are still evolving. The author surveys these efforts, their results and their limitations. In spite of recent advances in evaluation methodology, many uncertainties (including widely held but unsubstantiated beliefs) remain as to the effectiveness of spam filtering techniques and as to the validity of spam filter evaluation methods. Email Spam Filtering: A Systematic Review outlines several uncertainties and proposes experimental methods to address them. Email Spam Filtering: A Systematic Review is a highly recommended read for anyone conducting research in the area or charged with controlling spam in a corporate environment.

Embedded Systems and Artificial Intelligence
Embedded Systems and Artificial Intelligence

Author: Vikrant Bhateja

Publisher: Springer Nature

Published: 2020-04-07

Total Pages: 880

ISBN-13: 9811509476

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This book gathers selected research papers presented at the First International Conference on Embedded Systems and Artificial Intelligence (ESAI 2019), held at Sidi Mohamed Ben Abdellah University, Fez, Morocco, on 2–3 May 2019. Highlighting the latest innovations in Computer Science, Artificial Intelligence, Information Technologies, and Embedded Systems, the respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.

Deep Learning Illustrated
Deep Learning Illustrated

Author: Jon Krohn

Publisher: Addison-Wesley Professional

Published: 2019-08-05

Total Pages: 725

ISBN-13: 0135121728

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"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author: John D. Kelleher

Publisher: MIT Press

Published: 2020-10-20

Total Pages: 853

ISBN-13: 0262361108

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The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Machine Learning Techniques and Analytics for Cloud Security
Machine Learning Techniques and Analytics for Cloud Security

Author: Rajdeep Chakraborty

Publisher: John Wiley & Sons

Published: 2021-11-30

Total Pages: 484

ISBN-13: 1119764092

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MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.

Innovative Data Communication Technologies and Application
Innovative Data Communication Technologies and Application

Author: Jennifer S. Raj

Publisher: Springer Nature

Published: 2020-01-30

Total Pages: 852

ISBN-13: 3030380408

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This book presents emerging concepts in data mining, big data analysis, communication, and networking technologies, and discusses the state-of-the-art in data engineering practices to tackle massive data distributions in smart networked environments. It also provides insights into potential data distribution challenges in ubiquitous data-driven networks, highlighting research on the theoretical and systematic framework for analyzing, testing and designing intelligent data analysis models for evolving communication frameworks. Further, the book showcases the latest developments in wireless sensor networks, cloud computing, mobile network, autonomous systems, cryptography, automation, and other communication and networking technologies. In addition, it addresses data security, privacy and trust, wireless networks, data classification, data prediction, performance analysis, data validation and verification models, machine learning, sentiment analysis, and various data analysis techniques.