Demystifying Emerging Trends in Machine Learning (Volume 2) offers a deep dive into emerging and trending topics in the field of machine learning (ML). This edited volume showcases several machine learning methods for a variety of tasks. A key focus of this volume is the application of text classification for cybersecurity, E-commerce, sentiment analysis, public health and web content analysis.
 
 The 49 chapters highlight a wide variety of machine learning methods including SVNs, K-Means Clustering, CNNs, DCNNs, among others. Each chapter includes accessible information through summaries, discussions and reference lists. This comprehensive volume is essential for students, researchers, and professionals eager to understand the emerging trends reshaping machine learning today.
 
Readership
 
Scholars and professionals interested in machine learning trends and research.
 

Demystifying Emerging Trends in Machine Learning

End User License Agreement (for non-institutional, personal use)

A Method Based on Machine Learning to Classify Text for the Field of Cybersecurity

A Practicable E-commerce-Based Text-Classification System

AI Model for Text Classification Using FastText

An Algorithm for Textual Classification of News Utilizing Artificial Intelligence Technology

Analysis of the Sentiment of Tweets Regarding COVID-19 Vaccines Using Natural Language Processing and Machine Learning Sectionification Algorithms

Classification of Medical Text using ML and DL Techniques

Evaluation of ML and Advanced Deep Learning Text Classification Systems

Machine Learning Method Employed for the Objective of Identifying Text on Tweet Dataset

Textual Classification Utilizing the Integration of Semantics and Statistical Methodology

The Use of Machine Learning Techniques to Classify Content on the Web

Lexical Methods for Identifying Emotions in Text Based on Machine Learning

Identification of Websites Using an Efficient Method Employing Text Mining Methods

Machine Learning-based High-Dimensional Text Document Classification and Clustering

The Application of an N-Gram Machine Learning Method to the Text Classification of Healthcare Transcriptions

Method for Adaptive Combination of Multiple Features for Text Classification in Agriculture

Deep Learning-based Text-Retrieval System with Relevance Feedback

Domain Knowledge-based BERT Model with Deep Learning for Text Classification

Applying Deep Learning to Classify Massive Amounts of Text Using Convolutional Neural Systems

An Algorithm for Categorizing Opinions in Text from Various Social Media Platforms

Text Classification Method for Tracking Rare Events on Twitter

Text Document Preprocessing and Classification Using SVM and Improved CNN

Identification of Text Emotions Through the Use of Convolutional Neural Network Models

Classification & Clustering of Text Based on Doc2Vec & K-means Clustering based Similarity Measurements

Categorization of COVID-19 Twitter Data Based on an Aspect-Oriented Sentiment Analysis and Fuzzy Logic

Feature-Level Sentiment Analysis of Data Collected through Electronic Commerce

Classification Algorithms for Evaluating Customer Opinions using AI

Analysis of Sentiment Employing the Word2vec with CNN-LSTM Classification System

Hadoop-based Twitter Sentiment Analysis Using Deep Learning

A Contrast Between Bert and Word2vec's Approaches to Text Sentiment Analysis

Text Emotion Categorization Using a Convolutional Recurrent Neural Network Enhanced by an Attention Mechanism-based Skip-Gram Method

Multimodal Sentiment Analysis in Text, Images, and GIFs Using Deep Learning

Public Opinion Regarding COVID-19 Analyzed for Emotion Using Deep Learning Techniques

CNN-based Deep Learning Techniques for Movie Review Analysis of Sentiments

Machine Learning and Deep Learning Models for Sentiment Analysis of Product Reviews

Sentiment Analysis of Hotel Reviews Based on Deep Learning

Utilizing Machine Learning for Natural Language Processing to Conduct Sentiment Analysis on Twitter Data in Multiple Languages

The Use of Machine Learning to Analyze the Sentiment for Social Media Networks

Sentiment Classification of Textual Content using Hybrid DNN and SVM Models

Big Data Analysis and Information Quality: Challenges, Solutions, and Open Problems

Using Deep Learning Techniques to Detect Traffic Information in Social Media Texts

Deep Sentiment Classification in COVID-19 Using LSTM Recurrent Neural Network

Machine Learning-Based Data Preprocessing as well as Visualization Techniques for Predicting Students' Tasks

The Prediction of Faults Using Large Amounts of Industrial Data

Comparison Analysis of Logical Regression and Random Forest with Word Embedding Techniques for Twitter Sentiment Analysis

The Classification of News Articles Through the Use of Deep Learning and the Doc2Vec Modeling

Investigating the Utility of Data Mining for Automated Credit Scoring

Investigating the Use of Data Mining for Knowledge Discovery

Exploring the Role of Big Data in Predictive Analytics

Implementing Automated Reasoning in Natural Language Processing

Bentham Science Publishers

Demystifying Emerging Trends in Green Technology explores the transformative intersection of computational intelligence, disruptive technologies, and green innovations. This volume offers insights into diverse fields such as blockchain, IoT, artificial intelligence, machine learning, and sustainable development. Each chapter presents cutting-edge research and practical solutions addressing environmental sustainability, energy efficiency, and eco-friendly technologies.
With contributions from leading researchers, this book discusses advancements like blockchain-based security, green marketing, smart waste management, sustainable agriculture, and innovative healthcare solutions. It emphasizes the role of interdisciplinary approaches in driving a greener and smarter future.
 
Key Features:
- Integration of AI, IoT, and blockchain in sustainable systems
- Applications in healthcare, agriculture, energy, and environmental science
- Practical and innovative solutions for real-world challenges
- Insights into future trends in green technology and disruptive innovation
 
Readership:
Ideal for academics, researchers, professionals, and enthusiasts seeking to advance green technology practices.

Demystifying Emerging Trends in Green Technology

Pankaj Kumar Mishra

Satya Prakash Yadav

"Pragmatic Internet of Everything (IOE) has emerged as a powerful paradigm for representing and solving complex problems. This reference demonstrates how to coordinate behaviour among a collection of semi-autonomous problem-solving agents: how they can coordinate their knowledge, goals and plans to act together, to solve joint problems, or to make individually or globally rational decisions in the face of uncertainty and multiple, conflicting perspectives. 
 
 
 
The book presents a collection of articles surveying several major recent developments in Pragmatic Internet of Everything (IOE). The book focuses on issues and challenges that arise in building IOE systems for smart cities in real-world settings. It also presents solutions to the issues faced by system architects. The synthesis of recent thinking, both theoretical and applied, on major IOE problems makes this essential reading for anyone involved in the design and planning of IOT systems for smart cities.  
 
 
 
Key Features
 
-          Summarizes available literature and practical ventures with references
 
-          Merges different perspectives on IoT technology thereby giving a 360-degree perspective to the reader
 
-          Gives some tips for implementation of practical ventures in this space
 
-          Includes an analysis of information gathered from citizens of smart cities"


Pragmatic Internet of Everything (IOE) for Smart Cities: 360-Degree Perspective

"The book aims to provide a deeper understanding of the synergistic impact of Artificial intelligence (AI) and the Internet of Things (IoT) for disease detection. It presents a collection of topics designed to explain methods to detect different diseases in humans and plants. Chapters are edited by experts in IT and machine learning, and are structured to make the volume accessible to a wide range of readers.
 
 
 
Key Features:
 
-          17 Chapters present information about the applications of AI and IoT in clinical medicine and plant biology
 
-          Provides examples of algorithms for heart diseases, Alzheimer’s disease, cancer, pneumonia and more
 
-          Includes techniques to detect plant disease
 
-          Includes information about the application of machine learning in specific imaging modalities
 
-          Highlights the use of a variety of advanced Deep learning techniques like Mask R-CNN
 
-          Each chapter provides an introduction and literature review and the relevant protocols to follow
 
 
 
The book is an informative guide for data  and computer scientists working to improve disease detection techniques in medical and life sciences research. It also serves as a reference for engineers working in the healthcare delivery sector."


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Demystifying Emerging Trends in Machine Learning

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About the authors

Pankaj Kumar Mishra

Demystifying Emerging Trends in Green Technology

Pankaj Kumar Mishra

Satya Prakash Yadav

Satya Prakash Yadav