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Author: Mallikka Rajalingam
Publisher: Springer Nature
ISBN: 3030530477
Category : Technology & Engineering
Languages : en
Pages : 120
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Book Description
This book discusses email spam detection and its challenges such as text classification and categorization. The book proposes an efficient spam detection technique that is a combination of Character Segmentation and Recognition and Classification (CSRC). The author describes how this can detect whether an email (text and image based) is a spam mail or not. The book presents four solutions: first, to extract the text character from the image by segmentation process which includes a combination of Discrete Wavelet Transform (DWT) and skew detection. Second, text characters are via text recognition and visual feature extraction approach which relies on contour analysis with improved Local Binary Pattern (LBP). Third, extracted text features are classified using improvised K-Nearest Neighbor search (KNN) and Support Vector Machine (SVM). Fourth, the performance of the proposed method is validated by the measure of metric named as sensitivity, specificity, precision, recall, F-measure, accuracy, error rate and correct rate. Presents solutions to email spam detection and discusses its challenges such as text classification and categorization; Analyzes the proposed techniques’ performance using precision, F-measure, recall and accuracy; Evaluates the limitations of the proposed research thereby recommending future research.
Author: Mallikka Rajalingam
Publisher: Springer Nature
ISBN: 3030530477
Category : Technology & Engineering
Languages : en
Pages : 120
Get Book Here
Book Description
This book discusses email spam detection and its challenges such as text classification and categorization. The book proposes an efficient spam detection technique that is a combination of Character Segmentation and Recognition and Classification (CSRC). The author describes how this can detect whether an email (text and image based) is a spam mail or not. The book presents four solutions: first, to extract the text character from the image by segmentation process which includes a combination of Discrete Wavelet Transform (DWT) and skew detection. Second, text characters are via text recognition and visual feature extraction approach which relies on contour analysis with improved Local Binary Pattern (LBP). Third, extracted text features are classified using improvised K-Nearest Neighbor search (KNN) and Support Vector Machine (SVM). Fourth, the performance of the proposed method is validated by the measure of metric named as sensitivity, specificity, precision, recall, F-measure, accuracy, error rate and correct rate. Presents solutions to email spam detection and discusses its challenges such as text classification and categorization; Analyzes the proposed techniques’ performance using precision, F-measure, recall and accuracy; Evaluates the limitations of the proposed research thereby recommending future research.
Author: Dhavale, Sunita Vikrant
Publisher: IGI Global
ISBN: 1683180143
Category : Computers
Languages : en
Pages : 222
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Book Description
Security technologies have advanced at an accelerated pace in the past few decades. These advancements in cyber security have benefitted many organizations and companies interested in protecting their virtual assets. Advanced Image-Based Spam Detection and Filtering Techniques provides a detailed examination of the latest strategies and methods used to protect against virtual spam. Featuring comprehensive coverage across a range of related topics such as image filters, optical character recognition, fuzzy inference systems, and near-duplicate detection, this book is an ideal reference source for engineers, business managers, professionals, and researchers seeking innovative technologies to aid in spam recognition.
Author: Asit Kumar Das
Publisher: Springer Nature
ISBN: 9811930899
Category : Technology & Engineering
Languages : en
Pages : 692
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Book Description
This book features high-quality research papers presented at the 4th International Conference on Computational Intelligence in Pattern Recognition (CIPR 2022), held at Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal, India, during 23 – 24 April 2022. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.
Author: Zohra Saidane
Publisher: LAP Lambert Academic Publishing
ISBN: 9783844324617
Category : Graph theory
Languages : en
Pages : 156
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Book Description
Thanks to increasingly powerful storage media, multimedia resources have become nowadays essential resources and the challenge is how to quickly find relevant information. To accomplish this task, the text within images and videos can be a relevant key. In this work we focus on recognizing the content of the text and we assume that the text box has been detected and located correctly. We focused on a particular machine learning algorithm called convolutional neural networks (CNNs). These are networks of neurons whose topology is similar to the mammalian visual cortex. CNNs were initially used for recognition of handwritten digits. They were then applied successfully on many problems of pattern recognition. We propose in this work a new method of binarization of text images, a new method for segmentation of text images, the study of a convolutional neural network for character recognition in images, a discussion on the relevance of the binarization step in the recognition of text in images based on machine learning methods, and a new method of text recognition in images based on graph theory.
Author: Nadav Ben-Haim
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
This thesis addresses the problem of reading image text, which we define here as a digital image of machine printed text. Images of license plates, signs, and scanned documents fall into this category, whereas images of handwriting do not. Automatically reading image text is a very well researched problem, which falls into the broader category of Optical Character Recognition (OCR). Virtually all work in this domain begins by segmenting characters from the image and proceeds with a classification stage to identify each character. This conventional approach is not best suited for task specific recognition such as reading license plates, scanned documents, or freeway signs, which can often be blurry and poor quality. In this thesis, we apply a boosting framework to the character recognition problem, which allows us to avoid character segmentation altogether. This approach allows us to read blurry, poor quality images that are difficult to segment. When there is a constrained domain, there is generally a large amount of training images available. Our approach benefits from this since it is entirely based on machine learning. We perform experiments on hand labeled datasets of low resolution license plate images and demonstrate highly encouraging results. In addition, we show that if enough domain knowledge is available, we can avoid the arduous task of hand-labeling examples by automatically synthesizing training data.
Author: Dr. Amit Kumar Tyagi
Publisher: BPB Publications
ISBN: 9355516347
Category : Computers
Languages : en
Pages : 612
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Book Description
DESCRIPTION Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field. Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms. After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms. KEY FEATURES ● Basic understanding of machine learning algorithms via MATLAB, R, and Python. ● Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies. ● Adding futuristic technologies related to machine learning and deep learning. WHAT YOU WILL LEARN ● Ability to tackle complex machine learning problems. ● Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data. ● Efficient data analysis for real-time data will be understood by researchers/ students. ● Using data analysis in near future topics and cutting-edge technologies. WHO THIS BOOK IS FOR This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Statistical Analysis 3. Linear Regression 4. Logistic Regression 5. Decision Trees 6. Random Forest 7. Rule-Based Classifiers 8. Naïve Bayesian Classifier 9. K-Nearest Neighbors Classifiers 10. Support Vector Machine 11. K-Means Clustering 12. Dimensionality Reduction 13. Association Rules Mining and FP Growth 14. Reinforcement Learning 15. Applications of ML Algorithms 16. Applications of Deep Learning 17. Advance Topics and Future Directions
Author: Mark Stamp
Publisher: Springer Nature
ISBN: 3030625826
Category : Computers
Languages : en
Pages : 651
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Book Description
This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
Author: Narayan Changder
Publisher: CHANGDER OUTLINE
ISBN:
Category : Computers
Languages : en
Pages : 109
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Book Description
Embark on a transformative journey into the world of "NEURAL NETWORKS" with our definitive MCQ guide, "NeuroNexis." Tailored for AI enthusiasts, students, and professionals delving into the fascinating field of neural networks, this resource is your key to unraveling the intricacies of artificial intelligence, deep learning, and the revolutionary capabilities of neural network architectures. Dive into a knowledge-rich experience, progressing from foundational to advanced concepts through a series of thoughtfully curated multiple-choice questions. Key Features: MCQ Exploration: Navigate through a diverse array of questions covering fundamental principles, neural network architectures, and the unique characteristics of deep learning, ensuring a comprehensive understanding of this transformative field. Detailed Explanations: Elevate your knowledge with comprehensive explanations accompanying each MCQ, unraveling the intricacies of activation functions, backpropagation, and the principles that define the power of neural network computation. Real-World Applications: Bridge theory and practice, connecting neural network concepts to real-world applications in image recognition, natural language processing, and solving complex problems across various domains. Progressive Difficulty Levels: Challenge yourself with questions ranging from foundational to advanced, providing a structured learning experience suitable for learners at all levels. Visual Learning Tools: Reinforce your understanding with visual aids such as neural network diagrams, activation function graphs, and deep learning architecture illustrations, enhancing your grasp of neural network concepts. Embark on a quest for neural knowledge with "NeuroNexis: NEURAL NETWORKS." Download your copy now to master the essential skills needed for understanding the transformative potential of neural networks. Whether you're a student, AI enthusiast, or a professional in the field, this guide is your key to unlocking the capabilities of neural network architectures with precision and expertise.
Author: Rainer Lienhart
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: S. Kanimozhi Suguna
Publisher: CRC Press
ISBN: 1000375528
Category : Technology & Engineering
Languages : en
Pages : 331
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Book Description
This book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of Artificial Intelligence. The book provides a premier interdisciplinary platform to present practical challenges and adopted solutions. The book addresses the complete functional framework workflow in Artificial Intelligence technology. It explores the basic and high-level concepts and can serve as a manual for the industry for beginners and the more advanced. It covers intelligent and automated systems and its implications to the real-world, and offers data acquisition and case studies related to data-intensive technologies in AI-based applications. The book will be of interest to researchers, professionals, scientists, professors, students of computer science engineering, electronics and communications, as well as information technology.
