Analysis and Comparison of Various Web Mining Techniques

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

AI has rapidly changed how web applications are created and delivered in recent years. Using AI brings the user benefits of better automated processes through intelligent self-service, personalization of user experiences/customers, predictive analysis, and now includes real-time decision making. Before AI, web applications were mostly static or rules-based with limited or no change in response to how users interact with them. The introduction of advanced learning technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) has enabled web applications to become "intelligent" platforms that can receive input from a large volume of data and make adjustments in performance to continuously improve Keywords:Artificial Intelligence (AI), Web Applications, Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Recommendation Systems, Chatbots, Predictive Analytics, Data Mining.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

In the past decades, sentiment analysis has become one of the most active research areas in natural language processing, data mining, web mining, and information retrieval. The great demand in everyday life and the factor of novelty coupled with the availability of data from social networks have served as strong motivation for research on sentiment-analysis. A number of technical problems, most of which had not been attempted before, either in the NLP or linguistics communities have also generated strong research interests in academia. Sentiment analysis, also called opin-ion mining, is the field of study that analyzes people’s opinions, sentiments, apprais-als, attitudes, and emotions toward entities and their attributes expressed in written text. The entities can be products, services, organizations, individuals, events, issues, or topics. The field represents a large problem space. It improves not only the field of natural language processing but also management, political science, economics, and sociology because all these areas are related to the thoughts of consumers and public. User-generated content is full of opinions, because the main reason why people post messages on social media platforms is to express their views and opinions, and therefore sentiment analysis is at the centre of social media analysis. It turned out that user messages often contain plenty of sarcastic expressions and ambiguous words. Within one opinion both positive and negative sentiments can be present. This also applies to negative particles, which do not always indicate a negative tone. This article investigates four challenges faced by researchers while conducting sentiment analysis, namely: sarcasm, negation, word ambiguity, and multipolarity. These aspects significantly affect the accuracy of the results when we determine a sentiment. Modern approaches to solving the problem are also covered. These are mainly machine learning methods, such as convolutional neural networks (CNN), deep neural networks (DNN), long short-term memory (LTSM), recurrent neural network (RNN), support vector machines (SVM), etc.

Mark Watson Morgan Kaufmann Publishers, San Francisco, 1997, 377 pp. ISBN 1-55860-420-0, $44.95 This book is well written and organized to cover how to apply AI (Artificial Intelligence) techniques for practical Internet and Intranet tools by using the Java programming language. In the book, the author uses neural network and genetic algorithms to develop distributed systems that are applied for the Internet and Intranets, such as client/server model and information retrieval tool and so on. Part 1 begins with a good general introduction to AI. The history and survey of Artificial Intelligence research are given briefly. In addition, symbolic processing versus neural networks approaches are discussed. This author opts to use neural network to develop example Internet/Intranet systems in the book. Part 2 consists of Java programs developed for AI, neural networks, genetic algorithms, natural language processing, and expert systems. Chapter 2 illustrates Graphical User Interface (GUI) class that is derived from Applet and Canvas classes in Java, which is used in most example programs in the book. Chapter 3 introduces a frame data structure that is used as a message in distributed systems in the later chapters. Chapter 4 shows neural network classes developed in Java and its theory and implementation. Chapter 5 explains the genetic algorithms and the Java genetic algorithm classes. Chapter 6 describes how to develop and implement natural language processing classes using Java. Chapter 7 shows how to share data objects in distributed systems. For example, distributed AI agent classes are developed to share data objects, WWW data collection agent classes are shown to retrieve documents in the Internet, and small POP (Post Office Protocol) mail classes are developed to send and receive mails. Chapter 8 illustrates how to develop expert systems by using Java Jess, which is based on OPS5/CLIPS expert systems. Part 3 explains how to generate and implement the projects using the Java classes that are developed in Part 2. Chapter 9 shows the project that demonstrates the use of genetic algorithms in chapter 5 to control enemy ships in a scrolling arcade game. Chapter 10 describes the real-time handwriting recognition system developed by using the neural network classes in chapter 4. Chapter 11 illustrates a greedy algorithm to optimize neural networks that is based on the author’s hand-tuning neural training data. Chapter 12 improves neural network training data by applying genetic algorithms, to discard bad training data and to avoid discarding good training data. Chapter 13 describes the distributed natural language processing system that uses natural language processing classes in chapter 6. The data structures in chapter 2 are used to store historical data in a separate server application on any other computer in the Internet or Intranets. The final chapter provides the design and implementation of a complete agent-based information retrieval tool for the Internet. The retrieved documents are stored in ASCII or HTML format. This book is a very good reference book for anyone interested in developing systems using Java in distributed environments, especially applying AI techniques. Also, the author provides the CD-ROM that includes the source code for Java classes developed in the book. Haklin Kimm, University of Tennessee

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

There are various techniques for extracting relevant conclusions from various types of data the efficient approach that combines the power of data analysis, with intelligence and machine learning is smart web mining which enables the extraction of diverse data forms including text documents, videos and audio files and images effectively and efficiently. This study delves into the techniques and resources employed in web mining to accurately assess and comprehend data. Most web data is in the form of text. They are commonly fetched with algorithms like topic modelling, sentiment analysis, and other machine learning approaches known as natural language processing (NLP). These sources can appear to be inactive and unstructured, but indeed, these methods and other computer language model frameworks can decipher important information from them. For image data, algorithms for object detection use computer vision methods such as convolutional neural networks (CNN). While trying to detect patterns and information, faces or objects are identified and classified. Emotion, speaker identification, and automatic speech recognition are some of the applications of mining audio with neural networks, spectral, and speech to text analysis. The analysis of motion and interaction of objects in films is done using a fusion of visual and auditory analysis with guided methods. In building such frameworks, various algorithms giving the ability to process data from different sources are integrated. In handling the huge and heterogeneous information on the internet, intelligent web mining systems use the combination of the cloud computing service and computers that were built on the decentralized databases. Over and above sophisticated machine learning algorithms are trained with the datasets when the need for accuracy and flexibility is considered as the unsupervised learning technique reveals the hidden associations or patterns. This study highlights smart web mining's potential as a transformative tool for extracting insights from various data types, paving the way for innovative applications in a data-driven world.

AI’s 10 to Watch, 2022

The data mining (DM) is the computational process that consists of searching, extracting, and analyzing patterns in large data sets, including methods at the intersection of artificial intelligence, machine learning, statistics, and database schemes. Specifically, its primary goal is to extract information from a raw data set and transform it into an expected structure for further use. Moreover, an evolving perspective of DM is web mining (WM), which refers to the whole of DM and related routines. It is used to discover and extract information from web records and services automatically, that is, WM’s purpose is to obtain valuable data from the World Wide Web. Due to its importance, a survey about DM techniques in WM is necessary, as performed in this paper. Received: 30 June 2022 | Revised: 30 September 2022 | Accepted: 25 October 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.

A major challenge among various applications of computational linguistics, natural language processing and information retrieval is to measure semantic similarity accurately. In this research paper, various ontology-based approaches i.e. compute semantic similarity between words have been studied and listed their benefits and shortcomings on the various identified parameters. Earlier, correlation with human judgment was the single criteria for the judgment of good similarity measures. In this paper, more parameters for semantic similarity measures have been identified and a relative analysis of similarity measures is performed on the identified parameters. These identified parameters can be further utilized for formulating the new semantic similarity-measures in the latest research area of text mining, web mining and information retrieval. We have identified various parameters like features-set, applicability on various ontologies such as single ontology, cross ontology or fuzzy ontology, Ontology type, dataset applied and relationship mapping for the various measures. Through detailed analysis we have found that feature based and hybrid approaches has higher accuracy as compare to edge and content based methods and works in different type of ontologies. Recent research drawing interest to find new feature set in this area like fuzzy distance, graph generation and text snippets etc. Max Accuracy was achieved in single ontology 0.87 and 0.83 over cross ontologies. WordNet and MeSH are maximally utilized Global Ontologies.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Aim/Purpose: This study carried out a systematic review of the literature on artificial intelligence (AI) in English language teaching (ELT). The objective was to delineate the current research progress in the field and to further understand the challenges. Background: The study analyzed articles published between 2015 and 2021. Methodology: The qualitative research method was employed. Five steps were taken to steer the review. 200 articles were scrutinized; 64 were retained. Contribution: Prior research on AI in ELT has not investigated how the literature is progressing or what areas of AI are being covered. Without a holistic picture, some important research findings could be missed. Understanding how studies on AI in ELT are designed and implemented will contribute to a greater understanding of the existing state of research. Findings: Findings show that there is a promising future for AI in ELT. AI in ELT yielded positive results in terms of optimizing the English language skills, translation, assessment, recognition, attitude, satisfaction, etc. It was also found out that more and more articles on the topic are being published; the mixed research method is the most commonly used, higher education level is the most sampled, students as participants are the most sampled, and most studies developed novel AI-based systems. Various AI approaches have been identified in the reviewed studies, including machine learning, neural network, support vector machine, genetic algorithms, deep learning, decision tree, expert system, natural language processing, data mining, cloud computing, and edge computing. However, AI in ELT is still in its infancy, where little research has been conducted and gaps in the literature are still present, especially in terms of inherent issues related to body language, gestures, expressions, emotions, translation, lack of elaborate description of teaching material used for learning driven by AI, uncertainties and vagueness with regards to what can be considered under the realm of AI, and most authors being outside of the ELT discipline. Recommendations for Practitioners: This literature review is likely to provide practitioners with an overview of the current adopted technology, research method, instruments and/or tools, educational level, language skill, and the effects reported by the AI-based studies for designing effective systems for the use of AI in their ELT classrooms. Recommendation for Researchers: Researchers need to conduct research on AI in ELT along with a detailed in-depth description of the methodology, research design, and the proposed systems used to achieve AI in ELT. Furthermore, it is recommended that researchers explore the efficiency of AI-based systems used in previous research and ensure their relevance and functionality. They are also required to provide in-depth analysis of the challenges inherent to systems that have been highlighted in the literature, which will maximize the potentials of these AI-based technologies. Impact on Society: The findings of this paper can provide visualization of research findings that could particularly benefit researchers, educators, and AI specialists who are involved in the study of the applications of AI in ELT. Future Research: Future AI research needs to seriously include more detailed descriptions of the method in further research.

This paper presents a comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML), exploring foundational concepts, emerging trends, and diverse applications. AI and ML have rapidly evolved, becoming pivotal in numerous fields including healthcare, finance, manufacturing, and autonomous systems. The review begins by outlining key concepts, including the distinctions between AI, ML, and deep learning, and delves into various learning paradigms such as supervised, unsupervised, and reinforcement learning. It highlights significant advancements, such as neural networks, natural language processing, and generative models, emphasizing their impact on industry and research. The paper also examines current trends, including the rise of ethical AI, explain ability, and the integration of AI with Internet of Things (IoT) and edge computing, which are shaping the future landscape of AI applications. Additionally, it addresses the challenges and limitations associated with AI and ML, such as data privacy concerns, model interpretability, and the need for sustainable computing solutions. By synthesizing insights from recent literature, this review provides a holistic understanding of the AI and ML domains, offering perspectives on future directions and innovations. this review aims to provide a holistic understanding of AI and ML, offering perspectives on future directions and innovations.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

Data Mining refers to a set of methods applicable to large and complex databases to eliminate the randomness and discover the hidden pattern. Datamining (DM), also known as knowledge discovery from databases (KDD), is the extraction of new knowledge from huge databases. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Data mining tools can forecast the future trends and activities to support the decision of people. The scope of datamining is associated with Uncovering trends and patterns are a great power for the businesses of all sectors and industries. Modern intrusion detection applications are confronted with a variety of issues. These applications must be reliable, extensible, manageable, and minimal in maintenance costs. Data mining-based intrusion detection systems (IDSs) have shown high accuracy, good generalisation to novel types of intrusion, and stable behaviour in a changing environment in recent years. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear. The main purpose of this work is to identify privacy and security concerns among cloud computing participants and consumers in a distributed environment. Techniques like Machine Learning, Natural Language Processing (NLP), and Data Mining are combined to automatically identify and uncover patterns from many sorts of materials. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics. The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). The surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds. The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. Grid-based clustering has the shortest processing time, which is typically determined by the size of the grid rather than the data. We compare the performance of three clustering algorithms: hierarchical clustering, density-based clustering, and K Means clustering. The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. We provide a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques.