Reviewerly/exHarmony huggingface dataset:
Bio-Inspired Algorithms for Feature Selection

Feature selection is an important process of machine learning, especially when facing the high dimensionality challenges due the unprecedent increase of data, namely big data. The main objective of the feature selection process is to find the smaller feature subset which optimizes a learning algorithm performance providing by this a better readability. The feature selection problem is known to be an NP-hard problem, and classical approaches tackling it reached their limits. Therefore, tackling feature selection problem by bio-inspired algorithms has gained an increased interest due to the improved obtained results. This study presents an overview of feature selection problem and bio-inspired algorithms as a background. Based on this background, bio-inspired algorithms modeling elements for feature selection are described, followed by application domains and bio-inspired algorithms approach samples for feature selection problems. In addition, challenges and issues are discussed aiming to open future research opportunities.
Automatic Moderation of User-Generated Content

In recent years, most popular websites, such as Facebook and Wikipedia, depend on user involvement and content generation for their popularity and growth. Due to the vast expanse of these systems in terms of users and posts, manual verification of new content by the administrators or official moderators is not feasible, and these systems require scalable solutions. In this research, the authors show the emerging need for automated moderation of user-generated content using the latest machine learning and data science methods. This article will present novel ideas to create a real-world recommendation system that can assist system users and moderators in identifying the existing issues of old questions, sharing new high-quality questions, reducing the time needed for performing moderation actions, and improving the overall quality of the system.
Cluster Analysis as a Decision-Making Tool

Despite an increase in big data analytics research, many challenges still exist in designing the methodologies for enhancing big data analytical technologies towards achieving more informed decisions. This chapter addresses the frameworks to support the decision-making process by combining big data analytical techniques and decision theory. With the breakthrough of big data characteristics progressively, several new challenges appear. Clustering as an important tool has the capacity to support decision making processes. Consequently, this article explores the behaviour of different clustering methods and their algorithm approaches. The main gravity is to concentrate on the role of various cluster validity indices and their core principles to identify arrangements of patterns and interesting correlation in big data.
Reinforcement Learning for Combinatorial Optimization

Combinatorial optimization (CO) problems have many important application domains, including social networks, manufacturing, and transportation. However, as an NP-hard problem, the traditional CO problem-solvers require domain knowledge and hand-crafted heuristics. Facing big data challenges, can we solve these challenging problems with a learning structure within a short time? This article will demonstrate how to solve the combinatorial optimization problems with the deep reinforcement learning (DRL) method. Reinforcement learning (RL) is a subfield of machine learning (ML) that learns the optimal policy over time. Building on Markov decision process, RL has the solid theoretical foundation to obtain the optimal solution. Once parameters of DRL are trained, a new problem case can be solved quickly. Moreover, DRL learns the optimal solution without labels by maximizing the accumulative discounted reward received from the environment. This article will discuss three typical CO problems and present the advantages of DRL over other traditional methods.
Customer Analytics Using Sentiment Analysis and Net Promoter Score

In business, customer satisfaction with a product or service is essential. It is especially effective in campaigns to analyze customer sentiment and satisfaction with the brand or measure customer service quality. Nowadays, users can efficiently perform transactions such as shopping, ordering food and drink online, and then leave feedback on the company's e-commerce websites. Businesses want to analyze customers' opinions and feelings to determine users' sentiment and views towards a specific product or service. This study proposes a customer satisfaction analysis method based on sentiment analysis and net promoter score (NPS). First, a dataset consisting of 48,471 online reviews in Vietnamese on websites in the online food ordering service sector was collected. Next, the pre-processed data is put into experimental machine learning models to evaluate and select the best model. Experimental results show that the proposed method has an accuracy of up to 90%. Finally, NPS is calculated based on customer rating. The result is visualized on dashboards with critical information dimensions.
Cryptic Algorithms

Users that utilise cloud storage for various services can keep their files on the cloud and share their data with others. To ensure the integrity of data saved in cloud storage, a remote data integrity auditing method is employed. There are several methods for concealing sensitive information, but the most effective to date has been the use of a remote data integrity auditing technique. Another approach is to develop a remote data integrity auditing method that allows data exchange while keeping sensitive information hidden in this article. In this approach, a sanitizer module will sanitise the data blocks from the huge quantity of data saved that correspond to the file's sensitive information. In integrity auditing, this sanitised data is utilised to confirm the file's integrity. Outcome produced in the project makes this file stored in the cloud storage able to be shared and used by public users on the terms that the sensitive information is covered, where the remote data integrity auditing is still executed efficiently.
Ensemble Methods and Their Applications

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This article mainly focused on distinguishing between non-generative ensemble methods and generative ensembles. Non-generative ensemble methods embrace a large set of different approaches to combine learning machines. The subdivisions of non-generative strategies are ensemble fusion and ensemble selection methods. Generative ensemble methods generate sets of base learners acting on the base learning algorithm or on the structure of the data set and try to actively improve diversity and accuracy of the base learners. The subdivisions of generative strategies are resampling methods, feature selection methods, and output coding methods. The main aim of this article is to explain the detailed characteristics of each ensemble method and to provide an overview of the main application areas of ensemble methods.
Sentiment Analysis Using LSTM

Sentiment analysis is a vast growing field as various branches of artificial intelligence are being used in different fields. Incorporating sentiment analysis in the field of psychology would be of great help to both doctors for easy diagnosis and patients for self-checking. The research work proposed in this article focuses on obtaining the dependency of one word with others in its current context using long short-term memory (LSTM) for obtaining the sentiment. The dataset used consists of 156060 movie reviews and a model was trained to classify the cleaned text data into three classes – Negative, Neutral, and Positive. The performance of the model was evaluated by checking the loss generated and the validation accuracy after each epoch. The model and its hyper parameters were also tuned to obtain the best model in terms of validation accuracy.
Web Service in Knowledge Management for Global Software Development

Software development is a knowledge-intensive practice. Software development teams rely on human resources and systematic approaches to share knowledge on system design. This collaborative knowledge sharing and preserving mechanism is known as “knowledge management” in software industries. In the software development process, coordination of system design functionalities requires knowledge-sharing infrastructure within the team members. Semantic web service computing (SWSC) provides opportunities and value-added service capabilities that the global software development team requires to exchange information. This article describes the features of an ontology-based web portal framework, called CKIA (collaborative knowledge integration architecture), for integrating distributed knowledge in a global software development project. The CKIA framework uses a hybrid knowledge-based system consisting of structural case-based reasoning (S-CBR), rule-based reasoning (RBR), and an ontology-based concept similarity assessment mechanism. Finally, a business scenario demonstrates some of the system's functionalities.
Explainable Artificial Intelligence

The explainability of artificial intelligence (AI) is one of the central challenges for the wider use of the new technology in many industries and applications. The more powerful and efficient the algorithms of AI work, the less it is usually comprehensible to users. While there is widespread agreement on the basic requirement of explainability for AI applications, the design of an adequate AI explanation is rarely defined. This contribution presents basic concepts of explainability as well as current approaches to explanations for AI. It describes which methods are fundamentally suitable for considering an explanation to be complete and how it must be designed in order to be assessed as interpretable for AI.
Understanding Machine Learning Concepts

Artificial intelligence can be seen as the intelligence exhibited by machines. For an artificial intelligence system to be able to take decisions based on the data available, different type of learning methods, such as machine learning, need to be applied. Machine learning is a learning technique that gives machines the ability to learn without being explicitly programmed. It addresses the creation and study of algorithms that are capable of learning from data and making predictions about it. Machine learning algorithms can be divided into different categories including supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. In this article, the authors want to explain what machine learning is, as well as clearly establish the differences and relationship of machine learning with other related concepts, including artificial intelligence and deep learning. In addition, some possible use cases and applications will be named in order to provide the reader with a clear idea of what the potential of machine learning is.
Trust Management Mechanism in Blockchain Data Science

This article aims to apply blockchain data theories to trust management in a supply chain system to solve the trust crisis brought on by opportunistic behavior in supply chain management. Incremental implications are offered to increase supply chain managers' understanding of the underlying process of building trust using blockchain data technologies. A comprehensive literature review was undertaken to build a theoretical framework of how the trust management mechanism interacts with blockchain data and supply chain management. Design and methodology integrated multiple theories with the trust management application in supply malmanagement using blockchain data technology. This work identifies the coupling relationship between blockchain data and supply chain trust management and reveals the cultural, social, and economic attributes of trust manifested in interactions of the consensus-trust mechanism and interest motivations.
Big Data Mining and Analytics With MapReduce

Big data and machine learning are driving Industry 4.0. In the current era of big data, numerous rich data sources are generating huge volumes of a wide variety of valuable data at a high velocity. Embedded in these big data are implicit, previously unknown, and potentially useful information and knowledge. This calls for data science, which makes good use of big data mining and analytics, machine learning, and related techniques to mine, analyze, and learn from the data to discover hidden gems. This may maximize the citizens' wealth and/or promote all society's health. As an important big data mining and analytics task, frequent pattern mining aims to discover interesting knowledge in the forms of frequently occurring sets of merchandise items or events. To mine them in a scalable manner, several algorithms have used the MapReduce model. This encyclopedia article focuses on MapReduce-based frequent pattern mining from big data.
Bayesian Network-Based Decision Support for Pest Management

The Bayesian network can be used to estimate the effectiveness of alternate management decisions or policies because of being causal in nature. BNs have the ability to sustain clarity through explicit causal assumptions and so are generally used to model when the relationships to be described are not easily expressed using mathematical notation. Bayesian networks have many other attractive properties which make them particularly applicable in data analysis and decision making. BNs have simple causal and graphical structure and can be easily extended and altered. In this article, the authors discuss the concepts related to Bayesian networks and the role of Bayesian networks in development of decision support system for pest management.
An Agent and Pattern-Oriented Approach to Data Visualization

In order to analyze and visualize big data with a large variety of data, we need an agile and flexible software system. Following the traditional standard programming paradigm, such a system cannot easily be built. This article introduces a programming approach that combines generic programming, pattern-oriented programming, and agent-oriented programming. Reflection techniques that allow components and patterns to change their behavior depending on the data context are also presented. The main motivation of aspect-oriented programming, the separation of concerns, is also discussed. This approach provides guidelines for building flexible, extensible, and maintainable software systems. However, a paradigm shift in programming is required, which is also presented in this article.
A Comparison of SOM and K-Means Algorithms in Predicting Tax Compliance

Compliance with taxes has been associated with the perceived probability of detection and severity of punishment. Firstly, taxpayers comply because the benefits far outweigh the costs. Secondly, decisions to comply are closely related to the individual's risk aversion. Lastly, the availability of opportunities to cheat and the perceived probabilities of detection and sanctions have a significant impact on taxpayer compliance. In this study, two unsupervised learning algorithms were employed. The algorithms used were self-organizing map (SOM) and k-means clustering in predicting corporate income tax compliance. SOM and k-means are two notable unsupervised learning clustering techniques. In this article, some experiments have been conducted to compare their effectiveness and performance.
A Meta-Analytical Review of Deep Learning Prediction Models for Big Data

The article presents an introductory review of various approaches of deep learning including convolutional neural networks (CNNs), deep belief networks (DBNs), and auto-encoders (AEs). Each of these deep learning models is currently being used effectively in various fields such as medical application with healthcare systems, clinical trials, pharmacy industry, finance, agribusiness, energy industries, etc., and these models and all these models are extremely essential for any data scientist's toolbox. These deep learning models must build classes that should be flexibly designed, which can be useful in building new oriented application structure designs. Subsequently, for future development in the artificial intelligence-based technological world, it is important to have a necessary understanding of these deep learning models, which have been attempted to be refined through this systematic meta-analysis.
Foundational Recommender Systems for Business

Given the importance of recommender systems, a detailed understanding of the collaborative filtering systems is imperative. The standard formulation of most collaborative recommender algorithms is either based on prediction of a missing user-item rating or it tends to derive topmost k, users, or items. Collaborative methods could be further divided into user-based filtering and item-based filtering. Regression-based approach can help in combining the advantages of both user-based and content-based methods. Machine learning-based models on the other hand offer further generalization of above models and formally segregate the data modeling, training, and prediction phases. Latent factor models further the same idea by formally incorporating the dimensionality reduction concept and have been found to be very effective. The article is likely to be well received by the academics, especially the doctoral students/researchers in the field of recommender systems as well as the practitioners either utilizing or trying to procure recommendation systems for their organizations.
Cognitive Biases and Data Visualization

This article will provide background on cognitive biases related to data visualizations, with a particular interest towards visual analytics in big data environments. Cognitive biases that appear in visualizations will be discussed. A review of recent studies related to designing experiments to mitigate cognitive biases in data visualizations will be presented. Recommendations, using applied frameworks for detecting and mitigating biases in single visualizations and visual analytic systems, will be provided to practitioners.
Knowledge-Based Artificial Intelligence

Knowledge-based artificial intelligence has been extensively used in numerous application areas leading to the development of a vast number of methods and tools. In recent years, focus has shifted on non-symbolic approaches, and neural networks in particular have achieved human-level performance in various applications where accountability is a very important issue, closely related to the interpretability of artificial intelligence methods in general. Lack of interpretability of neural networks and various machine learning methods has led to the adoption of knowledge-based methods instead, which offer models compliant with explainability and interpretability requirements. In this article, an overview of knowledge-based methods is presented along with the state of the art in this area, offering to the AI practitioner guidance for applying these important methods in practice.
Online Educational Video Recommendation System Analysis

Most online platforms that provide video content, including TEDx, usually use various recommendation systems to gather more viewers. These videos are recommended based on various criteria. They can be either based on the user behavior and history of watched videos or on the basis of generally liked videos. The aim of this work is to conduct an in-depth analysis of the education platform called TEDx. This analysis will help in deriving the current protocols and thresholds this platform follows to curate and recommend videos to new users of the platform. The end goal is to figure out the various correlations between different parameters pertaining to these videos and on this basis to derive concrete illustrative representations of said relations and also to build a framework around these facts to find the exact relation between various videos on the platform.
AI Is Transforming Insurance With Five Emerging Business Models

Artificial Intelligence and related technologies are creating new opportunities and challenges for organizations across the insurance value chain. Incumbents are adopting AI driven automation at different speeds and new entrants are attempting to use AI to gain an advantage over the incumbents. This research explored four case studies of insurers’ digital transformation. The findings suggest that a technology focused perspective on insurance business models is necessary and that the transformation is at a stage where we can identify the prevailing approaches. The findings identify the prevailing five insurance business models that utilize AI: (1) Focus and disaggregation, (2) absorb AI into existing model, (3) incumbent expanding beyond model, (4) dedicated insurance disruptor and (5) tech company disruptor.
How Artificial Intelligence Is Impacting Marketing?

Artificial intelligence is a transformational general purpose technology that is impacting marketing as a function and marketing managers' activities, capabilities, and performance. The job of a marketing manager will be evolving into understanding which kind of artificial intelligence can and should be applied to which kind of marketing actions for better performance. Marketing managers will have to go through a learning curve and acquire new skills. The aim of this chapter is to present the tertiary literature on AI and to discuss the future of AI's impact in the field of marketing. The literature on AI for marketing is growing steadily. This article starts by describing some key concepts of the AI literature. It then illustrates a few important consequences of AI on businesses in general. Finally, it unfolds some of the important ways on how AI is already impacting, and will continue to impact, marketing managers' activities, capabilities, and performance. The article ends by discussing the future implication for marketing managers.
SampleMine

Subgraph Pattern Mining (SPM) is an important class of graph applications that aim to discover structural patterns in a graph. Due to the enormous exploration space, SPM is in general computationally challenging. To accelerate SPM, many random sampling techniques have been proposed. While the existing sampling techniques are effective for conventional SPM tasks such as motif counting and frequent subgraph mining, they cannot be easily adapted for new applications.
Research on WSN coverage optimization based on Improved PSO Algorithm

When PSO algorithm is applied to coverage optimization in wireless sensor networks, there will be some problems, such as slow convergence and premature phenomenon. To solve these problems, this paper proposes a wireless sensor network optimization algorithm based on standard PSO algorithm. The algorithm effectively improves the coverage rate of WSN network by improving the inertia weight and learning factor. Through simulation and analysis of the optimized coverage effect and convergence of coverage rate, the results show that the improved algorithm is better than the standard PSO algorithm and the improved algorithm in literature [4] in convergence speed and accuracy, and improves the problems of premature and slow convergence.
Dif-NoBa A differential privacy noise Bayesian gradient descent algorithm in Deep Learning

In recent years, differential privacy technology has been introduced into deep learning to satisfy the protection of personal privacy data. However, the existing differential privacy technology will weaken the generalization ability of deep learning networks. To address the problem, a differential privacy noise Bayesian gradient descent algorithm (Dif-NoBa) in Deep Learning was proposed. For the method, the privacy protection algorithm acts on a single neuron in the neural network. Therefore, no matter how complex the network is, it only focuses on a single neuron, so that each neuron can satisfy its own differential privacy, and then the whole network can meet the differential privacy, effectively solving the problem of insufficient generalization ability of neural network caused by adding arbitrary noise.