bluuebunny/crossref_metadata_2025 huggingface dataset:
Building Organizational DNA
Authors: Callinan Jill M. , Broadbent Michelle
Published: July 2022

To stay relevant in a world set on fast forward, organizations need to ensure they have all the critical components in place to stay relevant. More than just great products and services, organizations need to ensure they have the right mix of strategy, culture, leadership, and agility: the modern building blocks required for today's business continuity. This chapter will address how these components are interdependent and all contribute to creating an environment where people are connected and inspired.
Hybrid Leadership Styles Then and Now
Authors: Montaudon- Tomas Cynthia Maria , Pinto-López Ingrid N. , Amsler Anna
Published: July 2022

Although there are classical and modern leadership styles, from a historical perspective, hybrid leadership practices have been the norm. It is essential to state that the idea of hybrid leadership is not new, nor has it resulted from hybrid working conditions. This chapter analyzes hybrid leadership from different perspectives, tracing its origins, the start of publications in the field, and its evolution. A short bibliometric analysis is included to provide a general background about how the study of hybrid leadership has evolved. The different leadership styles and models connected to hybrid leadership will be analyzed, as well as its main characteristics. The goal is to present an overview of hybrid leadership and how it has been applied in different contexts.
Inclusion of People With Disabilities in Marketing
Authors: Sani Kareem F. , Gbadamosi Ayantunji
Published: July 2022

Whether mobility, auditory, sensory, visual, cognitive, or other types, living with a disability is a challenging experience. The individual dimension of this phenomenon is complemented by the social experience. In fact, as compared to individual experiences, social and political structures are considered as the leading cause of disability perception in society. Meanwhile, people with disability constitute a meaningful consumer segment with considerable purchasing power. However, extant literature indicates that the marketplace strategies are not friendly with people with disability in many areas. Hence, this chapter presents a conceptual and robust synthesis of these challenges and strategic directions for addressing the imbalance in relation to the equity and inclusion of people with disability in the marketing system. By and large, the chapter presents a paradigm shift on this phenomenon.
Young Consumer Vulnerability
Authors: Hsiao-Pei (Sophie) Yang  , Yiwen (Evie) Hong  , Kasapi Eleni
Published: July 2022

This chapter examined young consumer vulnerability by focusing on young consumers aged 11-17 to identify relevant issues associated with young consumer vulnerability from a marketing perspective. Young consumers are considered vulnerable when compared with adult consumers. Young consumers are commonly targeted by companies in the marketplace, from sectors such as beauty, luxury goods, alcohol, snack foods, e-sports, online gambling, or impulse purchases of other product categories. The authors discussed the potential issues of current marketing practices in targeting young consumers and the efforts that have been made so far by companies to address the issue of young consumer vulnerability by including five case company examples of young consumer inclusion. In addition, they discussed the ethical and marketing implications for brands in targeting young consumers, aiming for more equity and inclusion.
Technology and Marketing
Authors: Opute Abdullah Promise , Kalu Kalu Ibe , Chukwuma-Nwuba Eunice Oluwakemi , Ojra Jafar , Iwu Chux G.
Published: July 2022

Digital technologies have tremendously evolved and become a part of daily life. This constant evolution constitutes a major determinant of how consumers and organisations engage. For consumers, the evolvement intensifies their socialising capacity with customers of like mind, voice their dissatisfaction, and explore alternative options. Marketers leverage the digital evolvement to engage with customers, acquire marketing intelligence, and exploit customers' purchase behaviour emotions. Recognising the growing digital technologies trend and importance to marketing, this chapter highlights the need for more effort towards enhancing the technology-marketing interface. Given that movement restriction-induced consequences of COVID-19 have intensified the paradigm shift from physical to virtual marketing, this chapter forwards a theoretical framing that takes a technology-marketing viewpoint that focuses on customer engagement and ensuring implementation processes for effectively managing associated challenges. Finally, recommendations are offered and future research directions flagged.
Connecting Perspectives on Teacher Development
Authors: Strahan David
Published: July 2022

The author addresses the question, “How do teachers grow more accomplished as professionals?” A review of perspectives on teacher learning describes professional growth as a complex process that often proceeds in a progression “outward” (from self to tasks to students) and, at the same time, can progress “inward” (from concerns about capacity to manage classrooms to broader concerns about capacity to grow as a teachers). Research on accomplished teaching emphasizes complexity and variability, with identity and agency as essential factors in trajectories toward expertise. The author's own case studies with successful teachers demonstrate ways teachers enhance their professional identity and strengthen their sense of agency in the flow of classroom events and through thoughtful reflection. Based on these studies and the construct of pedagogically productive talk, he concludes that leaders can nurture professional development by providing time for teachers to talk about their work and support for collaboration.
New Teachers Thrive With Induction Support Through the Phases of a First Year Teacher
Authors: Wilcoxen Chris , Steiner Amanda , Bell Julie
Published: July 2022

Teacher attrition and support for novice teachers are impacted by quality induction programs. Induction programs provide support to beginning teachers through a job-embedded system of guidance and professional learning. Whereas Moir's phases of a first-year teacher framework outlined beginning teachers' developmental needs as a linear progression, the authors argue it is a dynamic process that occurs throughout the year. When new teachers are supported through these transitions with strategic mentoring and coaching practices, the transitions have less of a negative impact. Authors outline mentoring and coaching strategies to use during typical induction and crises to support beginning teachers in developing both resilience and self-efficacy.
Introduction to Two-Dimensional Inviscid Incompressible Flow
Authors: S. Kaliappan , M. D. Raj Kamal , V. Balaji , S. Socrates , Kondratiev Andrii
Published: July 2022

Here, the authors explain the Magnus effect. The ball is deflected in the same direction as the rotation. The most common exposure and welcome statement of the Magnus effect is that a spinning object creates a vortex of fluid swirling around it. On the side where the movement of the vortex is in the same direction as the direction of the flow to which the object is exposed, the speed will increase. On the opposite side, where the directions are opposite, the speed will decrease. It is explained here, according to Bernoulli's principle, that the pressure is lower on the side with the greatest velocity, and therefore, there is an unbalanced force orthogonal to the flow of the fluid.
Study of Drag Reduction on a Hypersonic Vehicle Using Aerospike
Authors: R. Naren Shankar , S. Irish Angelin , Pertsevyi Vitalii
Published: July 2022

Humans longing to fly higher and quicker have prompted the improvement of hypersonic vehicles. Typically, hypersonic streams are described by high temperature fields and a thin layer of shock close to the object wall or the body surface. To ease the reduction of thermal loads, a blunt nose is forced in a hypersonic vehicle which is more imperative. In any case, increase in the wave drag is one of the quick outcomes of a constrained bluntness. Consequently, investigation in the hypersonic field is constantly fixated on the wave drag decrease. The flow features around the blunt body get changed because of the attachment of spike in front of the vehicle. This chapter aims to give a detailed review of a hypersonic vehicle that involves an aerospike design in front of the blunt body, which tends to reduce the drag at the forebody. Views of various researchers are investigated, and efforts are taken to summarize the reported results on how the drag has been reduced using aerospike technique.
Computational Investigation on the Influence of Dump Gap and Novel Flame Tube Geometries in the Hybrid Diffuser Performance
Authors: Rose Bruce Ralphin , J. V. Bibal Benifa , M. Ramzania
Published: July 2022

The chapter addresses an extensive numerical investigation for the optimization of static pressure recovery in the dump diffuser of annular combustor for modern aero-gas turbine engines. The modelling and simulation processes are accomplished through the commercial ANSYS fluent module with SST k-epsilon (k-ɛ) turbulence model with the atmospheric conditions prevailing at 9 km altitude. The effect of velocity changes on the flow recirculation and vortex mixing phenomena is also addressed with novel dome-shaped optimization at the velocity of 25 m/s. The dome geometry can also be considered as a flame tube head, and the influence of vortex pattern and pressure distributions caused by the different dome shapes are analyzed using CFD. The dome-shaped optimization strategy addressed herein could initiate more potential studies on dump diffusers with hot and cold flow conditions to enhance the overall combustion efficiency in the near future.
Exploring the Socioeconomic Facet of Online Inclusive Education in Ghana
Authors: Ansu-Kyeremeh Eugene Kofi , Goosen Leila
Published: July 2022

Africa now stands at the threshold of development that requires a new approach to promote technological advancement and long-term economic growth. While information and communication technologies (ICTs) have been initiated in the education sector of Ghana, the technology evolution and its advancement remain sluggish due to a lack of productive ICT schemes and long-running support for ICT infrastructure. With the present global health crisis launching nationwide educational disruptions and causing numerous revisions in the system, there is a necessity for developing an understanding of the effects of these technological advancements. More importantly, the socioeconomic facet of online inclusive education demands a reevaluation to facilitate the conceptualization of effective teaching and learning activities. This chapter emphasizes that technology integration such as the application of online teaching and learning could be used to enhance digital literacy within the South African context.
Implications of the COVID-19 Pandemic on Higher Education in Tanzania
Authors: Bihu Reuben
Published: July 2022

The COVID-19 pandemic ushered in fundamental alterations that triggered social and economic crises. Unfortunately, the spread of the coronavirus likewise caused a dramatic disruption in the delivery of education services worldwide. As a response, many countries had to implement various safety measures such as nationwide lockdowns and school closures. The consequences of the pandemic were recognized in the teaching and learning processes as well as adjustments in the practices of administration and management of education services. As the largest educational disruption in human history, the pandemic signified a demand for an emergency preparedness, response, and recovery mechanism (EPRRM) contingency plan that may assist the education machinery in continuing the services despite emergencies and crises. This chapter reviewed the literature on the impact of the COVID-19 pandemic to draw a roadmap on how to develop an education EPRRM contingency plan by merging three core processes: learning, teaching, and administration.
How Inclusive Is Online Education in India
Authors: M. S. Navaneeth , Siddiqui Ismail
Published: July 2022

After years of mandatory remote education, there is still a question that remains to be answered: “Is the online medium inclusive enough to be deemed a solution?” As classrooms relocate to online environments and pedagogies rely on technologies, it becomes imperative to ensure that no one is left behind and education remains accessible to every learner. The issue of the universality of access to technological resources in India mingles with various socio-economic disparities that hinder the successful implementation of online education. This chapter attempts to analyze and bring forward the factors that may contribute to the stark contrasts regarding the practicality of the online education scene in India, including accessibility, gender, socio-economic factors, and policy issues. In mitigating the impact of an educational disruption (e.g., global health crisis) as well as the future adoption of online instruction, this chapter summarized the topics that need addressing into themes: content understanding, learning outcomes, technological, pedagogical, and behavioral.
Evaluation of the Experiences in the Restaurants With Multi-Criteria Decision-Making Methods
Authors: Tuş Ayşegül , Aytaç Adalı Esra
Published: July 2022

The restaurant selection is an important multi-criteria decision-making (MCDM) problem. Because many conflicting criteria affect the customers' restaurant selection, there are many alternatives to meet the customers' different and changing needs. The focus of this chapter is to search the criteria that influence the customer decisions to select a restaurant and evaluate the restaurants by utilizing the experiences of the customers. For this purpose, the restaurants located in Istanbul with online reservations that offer first-class dinners are considered as a sample case. The importance of the criteria is defined with the IVIF-CRITIC method, and the restaurants are ranked with the IVIF-CoCoSo method. IVIF sets are used to overcome the uncertainty due to the involvement of human judgment. It can be stated that the proposed methodology can be useful to analyze the human judgments in MCDM problems and develop the weak criteria for the restaurant managers and decide on restaurant choices for the customers.
An Experiential Investigation on Product Sampling in Retail Stores
Authors: Yılmaz Mustafa Atahan , Parıltı Nurettin
Published: July 2022

In-store product sampling is one of the most effective sales promotion tools by creating an impulsive urge to buy and interacting with the potential consumers at the point of purchase; however, it remains an understudied area for the academy. This study aims to investigate the difference in the purchase decision of the demographic/behavioral variables such as previous experience, gender, and age groups. In addition, the effects of in-store product sampling on sales are also examined. The findings demonstrated that the significant difference in purchase decisions is only between age groups. Furthermore, it is observed that the sales volume of the sampled product increased 10.8 times during the research week; however, the effect has not lasted long.
The Use of Pesticide Management Using Artificial Intelligence
Authors: Katiyar Sapna
Published: July 2022

The population of the world may reach almost 10 billion by 2050, and currently, approximately 37.7% of land is used for the production of crops. Agriculture is a major source of revenue for any country. Globally, automation in agriculture is in demand. Innovation and integration of technologies contributes for challenges faced by farmers with enlarged revenue and employment opportunities. Artificial intelligence has brought a revolution in agriculture. Crop wellbeing is important as it is a crucial factor that relates all parameters directly; therefore, crop health examination is mandatory. Premature detection of pests also reduces the quantity and frequent use of pesticides, but human intervention in process makes it time consuming and expensive. Time and techniques to use the pesticides in large farmland using AI along with computer vision and IoT converts traditional processes into smart agriculture. This chapter presents the assessment and implementation of an intelligent system for pesticide management.
Artificial Intelligence-Based Food Calories Estimation Methods in Diet Assessment Research
Authors: Begum Naimoonisa , Goyal Ankur , Sharma Sachin
Published: July 2022

The standard of healthy intake of food is the necessity for keeping a balanced diet to prevent the obesity problem and many other health problems in humans. Obesity is increasing at an alarming speed and keeping people's health at risk. Mankind needs to have careful control on their daily intake of calories by choosing healthier foods, which will be the most fundamental method in preventing obesity and ill health. Even though the packaging of food comes with calorie and nutrition labels, it might not be very favorable for the reference of people. Thus, the scientists to help people started using AI-based techniques and methodologies to know the ways of determining their daily calorie intake of their food. This chapter proposes a review of various AI-based food calorie estimation methodologies in diet assessment which are suggested to help the normal people and patients so that normal people and doctors could succeed to fight against diet-based health conditions.
Introduction to Machine Learning and Its Implementation Techniques
Authors: Arul Murugan R.  , Sathiyamoorthi V.
Published: July 2022

Machine learning (ML) is one of the exciting sub-fields of artificial intelligence (AI). The term machine learning is generally stated as the ability to learn without being explicitly programmed. In recent years, machine learning has become one of the thrust areas of research across various business verticals. The technical advancements in the field of big data have provided the ability to gain access over large volumes of diversified data at ease. This massive amount of data can be processed at high speeds in a reasonable amount of time with the help of emerging hardware capabilities. Hence the machine learning algorithms have been the most effective at leveraging all of big data to provide near real-time solutions even for the complex business problems. This chapter aims in giving a solid introduction to various widely adopted machine learning techniques and its applications categorized into supervised, unsupervised, and reinforcement and will serve a simplified guide for the aspiring data and machine learning enthusiasts.
Review and Applications of Machine Learning and Artificial Intelligence in Engineering
Authors: Yucel Melda , Bekdaş Gebrail , Nigdeli Sinan Melih
Published: July 2022

This chapter presents a summary review of the development of artificial intelligence (AI). The chapter provides definitions of AI, together with its basic features, and illustrates the development process of AI and machine learning. Further, the authors outline the developments of applications from the past to today and the use of AI in different categories. Finally, they describe prediction applications using artificial neural networks for engineering applications. The outcomes of this review show that the usage of AI methods to predict optimum results is the current trend and will be more important in the future.
A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm
Authors: Manukumar Shanthi Thangam , Muthuswamy Vijayalakshmi
Published: July 2022

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.
Machine Learning Applications for Anomaly Detection
Authors: Wahyono Teguh , Heryadi Yaya
Published: July 2022

The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.
Machine Learning Techniques for Analysis of Human Genome Data
Authors: Hiremath Neelambika Basavaraj , Dayananda P.
Published: July 2022

Human genome data analysis is one of the molecular level information in health informatics, which enables genetic epidemiological analysis of complex data sets. The recent studies of the genomic sequence, a part of genome-wide association studies (GWAS) have led to understand the genetic architecture to identify the area of focus i.e. interactions with single-nucleotide polymorphism (SNP) is linked to causing complex diseases. The study and identification of these interactions and splicing of nucleic acids involves complexity in processing and computation. This article reviews current methods and trends in various machine learning and data mining approaches which are very complex and challenging to model and evaluate the performances.
Knowledge Generation Using Sentiment Classification Involving Machine Learning on E-Commerce
Authors: Ghosh Swarup Kr , Dey Sowvik , Ghosh Anupam
Published: July 2022

Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have been demonstrated by appropriate features from raw unstructured data followed by data preprocessing steps. Applying best in class social analytics methodology to examine the sentiments embedded with purchaser remarks, encourages both producer and individual customers. Machine learning methods such as Naïve Bayes, maximum entropy classification, Deep Neural Networks were used upon the data, extracted from some websites such as Samsung and Apple for sentiment classification. In the online business arena, the application of sentiment classification explores a great opportunity. The subsidy of such an investigation is that associations can apply the proposed social examination framework to exploit the entire social information on the web and therefore improve their proper blueprint promoting strategies corresponding business.
Data Driven Symbiotic Machine Learning for the Identification of Motion-Based Action Potentials
Authors: Klinger Volkhard
Published: July 2022

Understanding and modelling technical and biological processes is one of the basic prerequisites for the management and control of such processes. With the help of identification, the interdependencies of such processes can be deciphered and thus a model can be achieved. The verification of the models enables the quality of the models to be assessed. This article focuses on the identification and verification of motion and sensory feedback-based action potentials in peripheral nerves. Based on the acquisition of action potentials, the identification process correlates physiological and motion-based parameters to match movement trajectories and the corresponding action potentials. After a brief description of a prototype of a biosignal acquisition and identification system, this article introduces a new identification method, the symbiotic cycle, based on the well-known term symbiotic simulation. As an example, this article presents a data-driven method to create a human readable model without using presampled data. The closed-loop identification method is integrated into this symbiotic cycle.
Hybridization of Machine Learning Algorithm in Intrusion Detection System
Authors: Amudha P.  , Sivakumari S.
Published: July 2022

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.
Machine Learning Techniques to Mitigate Security Attacks in IoT
Authors: S. Kavi Priya , K. Vignesh Saravanan , K. Vijayalakshmi
Published: July 2022

Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.
Investigating Machine Learning Techniques for User Sentiment Analysis
Authors: Patel Nimesh V , Chhinkaniwala Hitesh
Published: July 2022

Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.
Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia
Authors: Ahuja Ravinder , Vivek Vishal , Chandna Manika , Virmani Shivani , Banga Alisha
Published: July 2022

An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart diseases, anxiety, depression, and hypertension. Fifteen machine learning algorithms have been applied and 14 leading factors have been taken into consideration for predicting insomnia. Seven performance parameters (accuracy, kappa, the true positive rate, false positive rate, precision, f-measure, and AUC) are used and for implementation. The authors have used python language. The support vector machine is giving higher performance out of all algorithms giving accuracy 91.6%, f-measure is 92.13, and kappa is 0.83. Further, SVM is applied on another dataset of 100 patients and giving accuracy 92%. In addition, an analysis of the variable importance of CART, C5.0, decision tree, random forest, adaptive boost, and XG boost is calculated. The analysis shows that insomnia primarily depends on the factors, which are the vision problem, mobility problem, and sleep disorder. This chapter mainly finds the usages and effectiveness of machine learning algorithms in Insomnia diseases prediction.
Detection of Shotgun Surgery and Message Chain Code Smells using Machine Learning Techniques
Authors: Guggulothu Thirupathi , Moiz Salman Abdul
Published: July 2022

Code smell is an inherent property of software that results in design problems which makes the software hard to extend, understand, and maintain. In the literature, several tools are used to detect code smell that are informally defined or subjective in nature due to varying results of the code smell. To resolve this, machine leaning (ML) techniques are proposed and learn to distinguish the characteristics of smelly and non-smelly code elements (classes or methods). However, the dataset constructed by the ML techniques are based on the tools and manually validated code smell samples. In this article, instead of using tools and manual validation, the authors considered detection rules for identifying the smell then applied unsupervised learning for validation to construct two smell datasets. Then, applied classification algorithms are used on the datasets to detect the code smells. The researchers found that all algorithms have achieved high performance in terms of accuracy, F-measure and area under ROC, yet the tree-based classifiers are performing better than other classifiers.
Identifying Patterns in Fresh Produce Purchases
Authors: Bogomolov Timofei , Korolkiewicz Malgorzata W. , Bogomolova Svetlana
Published: July 2022

In this chapter, machine learning techniques are applied to examine consumer food choices, specifically purchasing patterns in relation to fresh fruit and vegetables. This product category contributes some of the highest profit margins for supermarkets, making understanding consumer choices in that category important not just for health but also economic reasons. Several unsupervised and supervised machine learning techniques, including hierarchical clustering, latent class analysis, linear regression, artificial neural networks, and deep learning neural networks, are illustrated using Nielsen Consumer Panel Dataset, a large and high-quality source of information on consumer purchases in the United States. The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications. The main outcome from predictive modelling of spending on fresh fruit and vegetables is that contrary to expectations, neural networks failed to outperform a linear regression model.
Machine Learning Algorithms
Authors: Dhanda Namrata , Datta Stuti Shukla , Dhanda Mudrika
Published: July 2022

Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.
Machine Learning for Web Proxy Analytics
Authors: Maldonado Mark , Barsoum Ayad
Published: July 2022

Proxy servers used around the globe are typically graded and built for small businesses to large enterprises. This does not dismiss any of the current efforts to keep the general consumer of an electronic device safe from malicious websites or denying youth of obscene content. With the emergence of machine learning, we can utilize the power to have smart security instantiated around the population's everyday life. In this work, we present a simple solution of providing a web proxy to each user of mobile devices or any networked computer powered by a neural network. The idea is to have a proxy server to handle the functionality to allow safe websites to be rendered per request. When a website request is made and not identified in the pre-determined website database, the proxy server will utilize a trained neural network to determine whether or not to render that website. The neural network will be trained on a vast collection of sampled websites by category. The neural network needs to be trained constantly to improve decision making as new websites are visited.
Machine Learning in Python
Authors: Baranwal Astha , Bagwe Bhagyashree R. , M Vanitha
Published: July 2022

Diabetes is a disease of the modern world. The modern lifestyle has led to unhealthy eating habits causing type 2 diabetes. Machine learning has gained a lot of popularity in the recent days. It has applications in various fields and has proven to be increasingly effective in the medical field. The purpose of this chapter is to predict the diabetes outcome of a person based on other factors or attributes. Various machine learning algorithms like logistic regression (LR), tuned and not tuned random forest (RF), and multilayer perceptron (MLP) have been used as classifiers for diabetes prediction. This chapter also presents a comparative study of these algorithms based on various performance metrics like accuracy, sensitivity, specificity, and F1 score.
Machine Learning for Health Data Analytics
Authors: Iyyanki Muralikrishna , Jayanthi Prisilla , Manickam Valli
Published: July 2022

At present, public health and population health are the key areas of major concern, and the current study highlights the significant challenges through a few case studies of application of machine learning for health data with focus on regression. Four types of machine learning methods found to be significant are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In light of the case studies reported as part of the literature survey and specific exercises carried out for this chapter, it is possible to say that machine learning provides new opportunities for automatic learning in expressive models. Regression models including multiple and multivariate regression are suitable for modeling air pollution and heart disease prediction. The applicability of STATA and R packages for multiple linear regression and predictive modelling for crude birth rate and crude mortality rate is well established in the study as carried out using the data from data.gov.in. Decision tree as a class of very powerful machine learning models is applied for brain tumors. In simple terms, machine learning and data mining techniques go hand-in-hand for prediction, data modelling, and decision making. The health analytics and unpredictable growth of health databases require integration of the conventional data analysis to be paired with methods for efficient computer-assisted analysis. In the second case study, confidence interval is evaluated. Here, the statistical parameter CI is used to indicate the true range of the mean of the crude birth rate and crude mortality rate computed from the observed data.
Challenges and Applications for Implementing Machine Learning in Computer Vision
Authors: Patel Hiral R. , Patel Ajay M , Parikh Satyen M.
Published: July 2022

The chapter introduces machine learning and why it is important. Machine learning is generally used to find knowledge from unknown data. There are many approaches and algorithms available for performing machine learning. Different kinds of algorithms are available to find different patterns from the data. This chapter focuses on different approaches with different usage.
Machine Learning and Its Use in E-Commerce and E-Business
Authors: Rath Mamata
Published: July 2022

Electronic commerce associated with highly powerful web technology and mobile communication is currently dominating the business world. Current advancements in machine learning (ML) have also further coordinated to creative business applications and e-commerce administrations to reason about complex system and better solutions. In the course of recent years, the business security and machine-learning networks have created novel strategies for secured business frameworks based on computationally learned models. With the improvement of the internet and digital marketing, every financial platform has been more secured and user friendly for monetary transactions.
Algorithmic Machine Learning for Prediction of Stock Prices
Authors: Beg Mirza O. , Awan Mubashar Nazar , Ali Syed Shahzaib
Published: July 2022

Stock markets and relevant entities generate enormous amounts of data on a daily basis and are accessible from various channels such as stock exchange, economic reviews, and employer monetary reports. In recent times, machine learning techniques have proven to be very helpful in making better trading decisions. Machine learning algorithms use complex logic to observe and learn the behavior of stocks using historical data which can be used to predict future movements of the stock. Technical indicators such as rolling mean, momentum, and exponential moving average are calculated to convert the data into meaningful information. Furthermore, this information can be used to build machine learning prediction models that learn different patterns in the data and make future predictions for accurate financial forecasting. Additional factors that are being used for stock prediction include social media influences and daily news on trading stocks. Considering these qualitative and quantitative features at the same time result in improved prediction models.
Prediction of High-Risk Factors in Surgical Operations Using Machine Learning Techniques
Authors: Anitha N.  , Devi Priya R.
Published: July 2022

Prediction of risk during surgical operations is one of the most needed and challenging processes in the healthcare domain. Many researchers use clinical assessment tools to predict perioperative outcomes and postoperative factors in surgical operations. Even though traditional model yields better results, they are not able to achieve promising accuracy due to the enormous growth of data in medical domain. Since the data size grows seamlessly every day, some of the investigators over the past decade use machine learning techniques in their model to predict the risks before and after surgery. Most of the existing systems produced better accuracy by impute missing values in dataset through some common imputation method. However, in order to increase the accuracy level further, two new techniques proposed in this chapter to handle missing values using iterative deepening random forest classifier and identification of surgical risk by using iterative deepening support vector machine. Both of the methods worked well in experimental data set and obtained promising accuracy results.
Data Mining and Machine Learning Approaches in Breast Cancer Biomedical Research
Authors: Chellamuthu Gunavathi , Kannimuthu S.  , Premalatha K.
Published: July 2022

Breast cancer is the most common invasive cancer in females worldwide. Breast cancer diagnosis and breast cancer prognosis are the two important challenges for the researchers in the medical field and also for the practitioners. If the cells in the breast start to grow without any control, it leads to cancer. Normally, the growth of the lump can be seen using x-ray. The benign and malignant breast lumps are distinguished during breast cancer diagnosis. The prognosis process predicts the period at which the breast cancer is likely to reappear in patients who have had their cancers removed. Data mining techniques and machine learning algorithms are mostly used in the whole process of breast cancer diagnosis and treatment. They utilize the large volume of breast cancer data for extracting knowledge. The application of data mining and machine learning methods in biomedical research is presently vital and crucial in efforts to transform intelligently all available data into valuable knowledge.
Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data
Authors: Gupta Madhuri , Gupta Bharat
Published: July 2022

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.
Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System
Authors: Riascos Alvaro J , Serna Natalia
Published: July 2022

Health-care systems that rely on hospitalization for early patient treatment pose a financial concern for governments. In this article, the author suggests a hospitalization prevention program in which the decision of whether to intervene on a patient depends on a simple decision model and the prediction of the patient risk of an annual length-of-stay using machine learning techniques. These results show that the prevention program achieves significant cost savings relative to several base scenarios for program efficacies greater than or equal to 40% and intervention costs per patient of 100,000 to 700,000 Colombian pesos (i.e., approximately 14% to 100% of the average cost per patient in Colombia statuary health care system). This article also shows how tree-based methods outperform linear regressions when predicting an annual length-of-stay and the final model achieves a lower out-of-sample error compared to those of the Heritage Health Prize.
Current Trends
Authors: Jagannathan Jayanthi , Anitha Elavarasi S.
Published: July 2022

This chapter addresses the key role of machine learning and artificial intelligence for various applications of the internet of things. The following are the most significant applications of IoT: (1) manufacturing industry: automation of industries is on the rise; there is an urge for analyzing the energy in the process industry; (2) anomaly detection: to detect the existing fault and abnormality in functioning by using ML algorithms thereby avoiding the adverse effect during its operation; (3) smart campus: in-order to efficiently handle the energy in buildings, smart campus systems are developed; (4) improving product decisions: with the help of the predictive analytics system products are designed and developed based on the user's requirements and usability; (5) healthcare industry: IoT with machine learning provides numerous ways for the betterment of the human wellbeing. In this chapter, the most predominant approaches to machine learning that can be useful in the IoT applications to achieve a significant set of outcomes will be discussed.
Big Data and Machine Learning
Authors: Martinez Fernando Enrique Lopez , Núñez-Valdez Edward Rolando
Published: July 2022

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.
COVID-19 Welcomes a New Variable That Influences Consumer Behavior
Authors: Blanco Alicia , Cachón-Rodríguez Gabriel , Cruz-Suárez Ana , Del-Castillo-Feito Cristina
Published: July 2022

COVID-19 has generated a context full of questions about the effects of mental health in the economic or social sphere. Significant changes in consumer behavior have been investigated. As consumers reduced their purchases in physical establishments and increased online purchases, retailers took measures to minimize health risks, but also to retain consumers. The objective of this research is to identify the intellectual structure of the research field on anxiety and consumption, including the main lines of research in the area, the sources of knowledge, and the connection points that are helping to spread this knowledge. To do this, this research uses a bibliometric methodology based on co-citations. This research concludes that academics must incorporate anxiety in their models and that companies must take this variable into account in the design of their business strategies. We have to legitimize anxiety as one more variable influences on consumer behavior.
The Hungarian Advertising Market and an Award-Winning Effie Case Study as Good Practice
Authors: Papp-Váry Árpád Ferenc , Kerti Réka
Published: July 2022

The advertising industry is an essential part of the Hungarian economy, which had an important role in rebuilding it after the COVID-19 pandemic. The industry is quick to adopt online and technological innovations. Digital media spend was the highest in 2020, and global partners such as Google and Facebook took the biggest slice. The case study introduces a recent EFFIE Hungary award-winning campaign with an unusual consumer review driving idea that relied on digital channels. The chapter fills gaps in the literature as well as provides essential information for global advertising teams.