الصفحة 3
الصفحة 3
Explainable AI with Python
This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others.
Evaluation of text summaries based on linear optimization of content metrics
Covers both theoretical contributions and practical applications in security system design by applying the Internet of Things (IoT) and CI. It further explains the application of IoT in the design of modern security systems and how IoT blended with computational intel- ligence can make any security system improved and realizable. Key features: Focuses on the computational intelligence techniques of security system design Covers applications and algorithms of discussed computational intelligence techniques Includes convergence-based and enterprise integrated security systems with their applications Explains emerging laws, policies, and tools affecting the landscape of cyber security Discusses application of sensors toward the design of security systems This book will be useful for graduate students and researchers in electrical, computer engineering, security system design and engineering
English proofreader
Using Natural Language Processing via Deep Learning, we will attempt to implement a context-sensitive spelling error correction system focused on casual text messaging. For example, usual autocorrect systems will not correct a sentence like “An apple is better than a banana” as this is a syntactically correct sentence. However, a correction should be made here to the word “then” and the sentence should be, “An apple is better than a banana.” These types of mistakes are common in people texting and can be found for example in people learning English as a second language. Natural Language Processing can be summarized as combining the utilization of computation to understand the concepts of natural language and speech.
Drousi: A private lessons management system
Private teaching is considered to be an effective way to increase academic achievement of students and it seen as being a supplementary education, so it has become very common all around the world, however, previous studies have come to different conclusions regarding its necessity, so Drousi application had been made ,it’s a user-friendly mobile application to manage and arrange private lessons, and There has never been a better time to become a private tutor, as class sizes in schools increase, this can result in teachers being unable to give their undivided attention to students, and this document lays out a project plan for the development of “Drousi”, The plan will include a summary of the system functionality and how it implemented.
Deep Learning with PyTorch Lightning : Build and train high-performance artificial intelligence and self-supervised models using Python
You’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
Deep learning approach for text summarization
Machine learning and deep learning, as we know, have started ruling over almost every field in the computing industry and so, has revolutionized the process of text summarization too. Automatic text summarization is an advancing realm of the natural language processing research in which concise textual summaries are generated from lengthy input documents. Extensive research has been carried out on how automatic summarization can be prosecuted through various extractive and abstractive techniques. In this paper, we address all the approaches to text summarization and present the modus operandi of an Architecture called Encoder Decoder, under the machine learning approach.
Data science for economics and finance : Methodologies and applications
The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis.
Data science ; 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020, Taiyuan, China, September 18-21, 2020, Proceedings, Part II
This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020. The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education.
Data Science ; 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020, Taiyuan, China, September 18-21, 2020, Proceedings, Part I
This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020. The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education.
Cybercrime in social media : Theory and solutions
Presents the important components for grasping the potential of social computing with an emphasis on concerns, challenges, and benefits of the social platform in depth. It discusses detection of social-cyber issues, including hate speech, cyberbullying, etc. using deep learning, natural language processing, etc.
Constraint solving and language processing
Contains selected and thoroughly revised papers plus contributions from invited speakers presented at the First International Workshop on C- straint Solving and Language Processing, held in Roskilde, Denmark, September 1–3, 2004. Constraint Programming and Constraint Solving, in particular Constraint Logic Programming, appear to be a very promising platform, perhaps the most promising present platform, for bringing forward the state of the art in natural language processing, this due to the naturalness in speci?cation and the direct relation to e?cient implementation. Language, in the present context, may - fer to written and spoken language, formal and semiformal language, and even general input data to multimodal and pervasive systems, which can be handled in very much the same ways using constraint programming. The notion of constraints, with slightly differing meanings, apply in the characterization of linguistic and cognitive phenomena, in formalized linguistic m- els as well as in implementation-oriented frameworks. Programming techniques for constraint solving have been, and still are, in a period with rapid devel- ment of new eficient methods and paradigms from which language processing can prompt. A common metaphor for human language processing is one big c- straint solving process in which the differently specified linguistic and cognitive phases take place in parallel and with mutual cooperation, which ?ts quite well with current constraint programming paradigms.
Computing Attitude and Affect in Text : Theory and Applications
Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the “factual” aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion. Various conceptual models and computational methods are presented.
Computer Vision Metrics : Survey, Taxonomy, and Analysis
Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more.
Computer and Information Sciences - ISCIS 2005 ; 20th International Symposium, Istanbul, Turkey, October 26 -- 28, 2005, Proceedings
This book constitutes the refereed proceedings of the 20th International Symposium on Computer and Information Sciences, ISCIS 2005, held in Istanbul, Turkey in October 2005. The 92 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 491 submissions. The papers are organized in topical sections on computer networks, sensor and satellite networks, security and cryptography, performance evaluation, e-commerce and Web services, multiagent systems, machine learning, information retrieval and natural language processing, image and speech processing, algorithms and database systems, as well as theory of computing.
Computational Processing of the Portuguese Language ; 8th International Conference, PROPOR 2008 Aveiro, Portugal, September 8-10, 2008 Proceedings
This book constitutes the thoroughly refereed proceedings of the 8th International Workshop on Computational Processing of the Portuguese Language, PROPOR 2008, held in Aveiro, Portugal, in September 2008.
Computational Processing of the Portuguese Language ; 7th International Workshop, PROPOR 2006, Itatiaia, Brazil, May 13-17, 2006, Proceedings
Since 1993, PROPOR Workshops have become an important forum for - searchers involved in the Computational Processing of Portuguese,both written and spoken. The workshop and this book were structured around the following main t- ics, seven for full papers: (i) automatic summarization; (ii) resources; (iii) au- matic translation; (iv) named entity recognition; (v) tools and frameworks; (vi) systems and models; and another ?ve topics for short papers; (vii) information extraction; (viii) speech processing; (ix) lexicon; (x) morpho-syntactic studies; (xi) web, corpus and evaluation.
Computational linguistics and intelligent text processing ; Vol. 3878 ; 7th International Conference, CICLing 2006, Mexico City, Mexico, February 19-25, 2006, Proceedings
CICLing 2006 (www.CICLing.org) was the 7th Annual Conference on Intelligent Text Processing and Computational Linguistics. The CICLing conferences are intended to provide a wide-scope forum for discussion of the internal art and craft of natural language processing research and the best practices in its applications.
Computational linguistics and intelligent text processing ; 9th International Conference, CICLing 2008, Haifa, Israel, February 17-23, 2008. Proceedings
The CICLing conferences are intended to provide a wide-scope forum for the discussion of both the art and craft of natural language processing research and the best practices in its applications. This volume contains the papers accepted for oral presentation at the c- ference, as well as several of the best papers accepted for poster presentation.
Computational linguistics and intelligent text processing ; 8th International Conference, CICLing 2007, Mexico City, Mexico, February 18-24, 2007, Proceedings
This book cover all current issues in computational linguistics research and present intelligent text processing applications. The papers are organized in topical sections on: lexical resources, corpus-based knowledge acquisition, morphology and part-of-speech tagging, named entity recognition, temporal expression treatment, word segmentation, sentence splitting, chunking, grammar formalisms and syntax, word sense disambiguation and discrimination and semantics.
