bluuebunny/crossref_metadata_2025 huggingface dataset:
Further results on stability analysis of Takagi–Sugeno fuzzy time-delay systems via improved Lyapunov–Krasovskii functional
Authors: Datta Rupak , Saravanakumar Ramasamy , Dey Rajeeb , Bhattacharya Baby
Published: July 2022

The problem of delay-range-dependent (DRD) stability analysis for continuous time Takagi–Sugeno (T–S) fuzzy time-delay systems (TDSs) is addressed in this paper. An improved DRD stability criterion is proposed in an linear matrix inequality (LMI) framework by constructing an appropriate delay-product-type (DPT) Lyapunov–Krasovskii functional (LKF) to make use of Bessel-Legendre polynomial based relaxed integral inequality. The modification in the proposed LKF along with the judicious choice of integral inequalities helps to obtain a less conservative delay upper bound for a given lower bound. The efficacy of the obtained stability conditions is validated through the solution of three numerical examples.
Ulam stability of hom-ders in fuzzy Banach algebras
Authors: Kheawborisut Araya , Paokanta Siriluk , Senasukh Jedsada , Park Choonkil
Published: July 2022

This paper aims to investigate a new type of derivations in a fuzzy Banach algebra. Moreover, by using the fixed point method, we obtain some stability results of the hom-der in fuzzy Banach algebras associated with the functional equation

   \begin{document}$ f(x+{\textbf{k}}y) = f(x)+{\textbf{k}}f(y) $\end{document}

where $ {\textbf{k}} $ is a fixed positive integer greater than $ 1 $.
Expected Bayesian estimation for exponential model based on simple step stress with Type-I hybrid censored data
Authors: Nagy M. , Abu-Moussa M. H. , Alrasheedi Adel Fahad , Rabie A.
Published: July 2022

The procedure of selecting the values of hyper-parameters for prior distributions in Bayesian estimate has produced many problems and has drawn the attention of many authors, therefore the expected Bayesian (E-Bayesian) estimation method to overcome these problems. These approaches are used based on the step-stress acceleration model under the Exponential Type-I hybrid censored data in this study. The values of the distribution parameters are derived. To compare the E-Bayesian estimates to the other estimates, a comparative study was conducted using the simulation research. Four different loss functions are used to generate the Bayesian and E-Bayesian estimators. In addition, three alternative hyper-parameter distributions were used in E-Bayesian estimation. Finally, a real-world data example is examined for demonstration and comparative purposes.
The New Rules of Engagement
Authors: Reed Dave
Published: July 2022

This chapter combines facts, nuances, and implications of the Turnover Tsunami and Great Resignation phenomena. The author provides important insights that connect changes at social and generational levels—notably regarding stress and burnout, engagement and commitment, and socio-technical work trends such as virtual work and the metaverse—to current and future employment challenges. The chapter is designed to open lines of sight into the most complex and dynamic times ever seen in the employment realm. The author infuses personal strategy (e.g., know your people), commentary (e.g., collective collusion), and applied theory (e.g., cognitive dissonance) to open pathways of thought, understanding, and exploration into mid-post-pandemic employment issues.
Young Consumers, Deprivation, and Christmas Commercialisation
Authors: Mingazova Diliara , Gbadamosi Ayantunji
Published: July 2022

Christmas is a special period in most countries of the world for many consumers. It is often associated with consumption on a considerable scale. This is especially true for children who are driven by a high need for affiliation which prompts their requests for goods and services that will give them the opportunity to interact with other children. Achieving this is a great challenge for many children in various households due to their ongoing experience of deprivation which comes in various ways such as food security, fuel poverty, and presents or gifts. This chapter is an eclectic synthesis of these issues to highlight the imbalance in society as these children experience exclusion from the mainstream commercial system as a result of poverty. The specific relevance of consumer culture theory (CCT) is linked to the commercialization of Christmas and living in economically disadvantaged circumstances in relation to this consumer segment. The implications of the discourse in this chapter are far-reaching to many ramifications.
The Collaborative Use of Programmatic Data in Efforts to Support Teacher Candidate Development
Authors: Zimmerman Aaron Samuel
Published: July 2022

Educational leaders and teacher educators face a very real dilemma within the current teacher education policy context, as there currently exist numerous accreditation and accountability mandates for which teacher education programs are responsible. Many of these accountability pressures require teacher educators and teacher education programs to provide evidence of consistent data use for continuous program improvement. Teacher education programs are also increasingly responsible for providing evidence-based measures of the quality of their teacher candidates. These data-driven expectations can create additional pressures, stresses, and workload responsibilities for teacher educators and teacher education program leaders. By drawing on the lessons learned from three exemplary teacher education programs, this chapter will discuss the benefits and the challenges related to organizing collaborative data use efforts within teacher education programs for the purpose of fostering teacher candidate development.
Creating a Support System That Launches the Paraprofessional-to-Teacher in a Grow Your Own Program
Authors: Jack Ashlie R. , Thiele Julie , Wilson Kimberly
Published: July 2022

As teacher shortages continue to increase in the wake of the pandemic and an increased number of retirement-eligible teachers take advantage of the benefits of retiring, more communities and school districts are looking toward their local university for teacher education pathway programs to attract and prepare paraprofessionals (paraeducators) as a certified teacher while remaining in the community and employed within the school as a paraprofessional or teacher-of-record on a restricted license. Traditionally, these types of grow your own (GYO) programs attract and admit non-traditional candidates who have not attended school in more than five years and have other life factors that require a network of support not always found in a traditional teacher education program.
Grow Your Own
Authors: Barron Lisa , Chandler Prentice T. , Impeartrice Sean , Bellamy Mason
Published: July 2022

This chapter focuses on two persistent challenges in teacher education and the teaching profession, specifically the development and retention of quality teachers. It examines the challenges that school districts and education preparation providers (EPPs) face in recruiting and preparing diverse candidates for hard-to-fill content areas and schools. This work began when a college of education and a school district collaboratively worked to design a new grow your own (GYO) model where recent high school graduates and paraprofessionals are paid to work with the district's best teachers, while earning an accelerated bachelor's degree, with all tuition, fees, and textbooks provided. This racially diverse cohort was dually licensed to teach in elementary (K-5) and special education and placed in the lowest socioeconomic schools in the districts with the commitment to teach in the district for at least three years.
Teacher Leadership
Authors: Hughes Marie Tejero , Passmore Amanda H. , Maggin Daniel M.
Published: July 2022

The development of special education teacher leaders has been a challenge in part due to difficulties in retaining special educators in the field. Special educators report several reasons for leaving the profession including not having opportunities for individual development and lacking influence. This high level of special educator turnover impacts the development of special education teacher leadership, given that many leave before they are fully established. In this chapter, the authors highlight what was learned from a series of research studies that focused on gaining an understanding of special education teacher leadership in schools. The authors also outline the findings from discussions with special educators at different stages of their careers regarding their aspirations and ways to support their career development in relation to teacher leadership. The chapter concludes by discussing some professional development opportunities that the authors have engaged in with special educators to develop leadership skills and experiences.
Creating Teacher Leaders Through Early Teacher Support
Authors: Hoffmann Malia M.
Published: July 2022

The study of pedagogical and content knowledge has been around since the late 1980s. As technology grows in popularity, it is natural to merge the two ideas. Using technological pedagogical and content knowledge (TPACK) in teacher leadership initiatives programs can help create teacher leaders by satisfying a needed area of expertise. By informing new educators on TPACK, they will begin to understand and utilize this framework, apply it to their instruction, support their colleagues, and become leaders with training and mentorship. Additionally, understanding the differences in technology use according to the SAMR model—substitution, augmentation, modification, and redefinition—will provide technology instruction in more advanced ways. Through this training and mentorship model, educators work together to develop their technological pedagogy and content knowledge in a dual mentorship of masters and apprentices within a community of practice.
High-Speed Flow Over Airfoils, Wings, and Airplane Configurations
Authors: Uppu Shiva Prasad , R. K. M. Sree Vaibhav , N. Dilip Raja , K. Sathish Kumar
Published: July 2022

Progress in future aeronautics depends purely on the new understandings of flow physics coupled with the interactions of various tools and disciplines. Emerging numerical computing tools and experimental aptitudes play a key role in the technological progress of aeronautical studies. This chapter presents an insight on the air foils, three-dimensional geometries attached to an airplane with an emphasis on computational tools. A countless number of small and large steps have taken place over many other disciplines. Design evolution has resulted in many geometrical changes in air foils, wings, fuselages, and stabilizers come in a whole range of shapes and sizes, both in the aerospace industry and in nature – really, nothing is standard. The application the airfoil operates and dictates its shape and size. Finite wing and infinite wing shapes are still sprouting today, driving the new challenging flight conditions. More efficient flights will drive the new and intelligent wing designs to obtain better load factor and reduced drag.
Compulsory Distance Learning in Uzbekistan During the COVID-19 Era
Authors: Khusanov Kasim , Khusanova Guljakhon , Khusanova Mushtariybonu
Published: July 2022

Digital transformation is not a visitor in the education sector as it has introduced a variety of changes from digitizing curriculums to enabling students to learn from a distance. Unfortunately, the COVID-19 pandemic has led to dramatic educational changes that position online instruction as the primary mode of teaching and learning worldwide. With nationwide lockdowns and movement restrictions, compulsory distance education became the means to ensure continuity of educational services. This chapter investigated this forced transition from face-to-face to online education with a particular emphasis on public and senior secondary vocational education systems in Uzbekistan. In addition to the consequences of the sudden shift to distance education, this chapter likewise examined the exacerbation of issues concerning inclusive education. The findings of this chapter underscored the detrimental effects of emergency remote education in Uzbekistan as well as the responses made to adapt to the digital learning environments.
Digital Transformation for Improving Customer Experience
Authors: Huseynli Bahman
Published: July 2022

Customer experience is defined as the customer's perceived overall quality of all interactions and relationships that the customer has with a company's products and services. Business leaders have increasingly begun to attach importance to the role of customer experience in helping companies gain competitive advantage. Also, the advantages and disadvantages of digitalization affect the customer experience process. In this respect, companies and brands that try to offer a unique experience for their customers need to prioritize the customer experience. So, in this chapter, the contributions of digitalization to customer experience are investigated. First, the theoretical infrastructure of customer experience and the creation of customer experience are examined. Afterward, customer journey and customer experience are discussed, and customer experience, touchpoints, and mapping of the customer journey are discussed. After that, digitalization and customer experience and the creation of the digital customer experience have been examined, and the conclusion has been reached.
Online Game Experiences
Authors: Eru Oya
Published: July 2022

The fact that businesses begin to reach consumers through online media and digital channels triggers consumers to experience new experiences. Therefore, creating new experiences for consumers in the online environment and determining which factors affect consumer experiences in the online environment gain importance for marketing managers in determining forward-looking strategies. With the digital transformation experienced, the game industry's becoming an increasingly important market has enabled the concept of games to become a marketing tool. This affects consumer behavior, experience, and satisfaction. So, one way to create new experiences for consumers online is to use games and new digital marketing tools integrated into the game concept. From this point of view, in this chapter, game market, game concept, game experience, gamification, product placement advergame, flow theory, and motivation theory will be discussed in terms of experiential marketing.
A Commercial Stage Strengthening Brand Image in the Framework of Postmodern Consumption
Authors: Akyol Cansu , Tuğlu Karslı Umut
Published: July 2022

In the postmodern era, competition in the retail sector has increased, and it has gained importance to be in the minds of the target audience as a result of the changing consumer profile, technological developments, and product diversity. This change has also affected retail formats, and pop-up spaces have become one of the marketing methods of businesses. Brands that want to stand out from the competition try to create memorable customer experiences with a new reality and a different perception environment, as well as an immersive atmosphere in the commercial space. Given the role of presentation and special effects, the interior becomes an exciting scene that strengthens the brand image. The study aims to examine the brand image-space relationship in the postmodern era within the framework of the different experiences offered to the customer through the metaphor of theater and to reveal the spatial elements that affect the customer's perception of space in pop-up spaces. As a result of the study, it is aimed that the elements will be a guide for pop-up retail design.
Freshness Grading of Agricultural Products Using Artificial Intelligence
Authors: Elbir Zeynep , Caferoglu Berat Asrin , Cihan Onur
Published: July 2022

This chapter presents a deep learning model that can be used to determine the levels of freshness of three agricultural products: strawberries, lemons, and tomatoes. For this purpose, YOLO, a state-of-the-art object detection algorithm is utilized. The data for training, validation, and testing are collected from online sources, and by applying image augmentation techniques, a sufficient number of images are obtained. Test results show that the model is performing quite well, and the speed of the model is fast. These results are promising and can be utilized to reduce a significant amount of agricultural waste and increase customer satisfaction once it is utilized by online groceries.
Precision Farming Using Image Processing and Machine Learning
Authors: Naaz Ayesha , Sucharitha B.
Published: July 2022

Agriculture in general is plagued by numerous problems that can be solved using modernization techniques and help the farmers making them aware of problems related to crop yield, nutrient status, crop disease, etc. Precision agriculture is a promising approach to address these issues. The objective of the chapter is design and deployment of farmer-friendly precision farming using multispectral imaging techniques for providing advanced, smart, and connected agricultural management solutions by integrating high-precision hardware, software, and data. Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision agriculture. Of the multiple technologies used for remote sensing, multispectral and hyperspectral remote sensing are widely in demand. The data is processed employing deep learning to allow multispectral image classification based on spectral-spatial features. Applying data analytics on the mapped data can provide suggestions/notifications/alerts to the farmers.
An Extensive Analysis of Flying Ad-Hoc Network Applications and Routing Protocols in Agriculture
Authors: Ghosh Ahona , Mistry Caprio , Biswas Mousumi
Published: July 2022

Agriculture and irrigation are sources of man's potential. For the sake of cost-effectiveness and the betterment of agricultural professionals, UAVs (unmanned aerial vehicles) can be deployed for surveillance, utilization of pesticides and insecticides, and detection of bioprocessing errors. With the proper collaboration and coordination of the clusters of UAVs forming a network, linked with a ground infrastructure or satellite will exceed the competencies of a single UAV system. However, one of the vital design issues FANETs deal with is in selecting the accurate routing protocol which is a prerequisite for the creation of FANET. In this chapter, the authors discuss the routing protocols of FANET in different platforms and different strategic manners. The open research challenges have been discussed and possible solutions have been attempted to be drawn from the conclusion. The main contribution lies in suggesting the most suitable routing protocol for each particular agriculture application based on the mobility model and requirement.
Machine Learning Applications in Nanomedicine and Nanotoxicology
Authors: Casañola-Martin Gerardo M. , Pham-The Hai
Published: July 2022

The development of machine learning algorithms together with the availability of computational tools nowadays have given an increase in the application of artificial intelligence methodologies in different fields. However, the use of these machine learning approaches in nanomedicine remains still underexplored in certain areas, despite the development in hardware and software tools. In this review, the recent advances in the conjunction of machine learning with nanomedicine are shown. Examples dealing with biomedical properties of nanoparticles, characterization of nanomaterials, text mining, and image analysis are also presented. Finally, some future perspectives in the integration of nanomedicine with cloud computing, deep learning and other techniques are discussed.
Classification and Machine Learning
Authors: Alberto Damian
Published: July 2022

The manual classification of a large amount of textual materials are very costly in time and personnel. For this reason, a lot of research has been devoted to the problem of automatic classification and work on the subject dates from 1960. A lot of text classification software has appeared. For some tasks, automatic classifiers perform almost as well as humans, but for others, the gap is still large. These systems are directly related to machine learning. It aims to achieve tasks normally affordable only by humans. There are generally two types of learning: learning “by heart,” which consists of storing information as is, and learning generalization, where we learn from examples. In this chapter, the authors address the classification concept in detail and how to solve different classification problems using different machine learning techniques.
Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security
Authors: Mkrttchian Vardan , Gamidullaeva Leyla Ayvarovna , Kanarev Sergey
Published: July 2022

The literature review of known sources forming the theoretical basis of calculations on Sleptsova networks and on the basis of authors' developments in machine learning with avatar-based management established the basis for the future solutions to hyper-computations to support cyber security applications. The chapter established that the petri net performed exponentially slower and is a special case of the Sleptsov network. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors conclude that machine learning with avatar-based management at the platform of the Sleptsov net-processor is the future solution for cyber security applications in Russia.
Architecting IoT based Healthcare Systems Using Machine Learning Algorithms
Authors: Karthick G. S. , Pankajavalli P. B.
Published: July 2022

The rapid innovations in technologies endorsed the emergence of sensory equipment's connection to the Internet for acquiring data from the environment. The increased number of devices generates the enormous amount of sensor data from diversified applications of Internet of things (IoT). The generation of data may be a fast or real-time data stream which depends on the nature of applications. Applying analytics and intelligent processing over the data streams discovers the useful information and predicts the insights. Decision-making is a prominent process which makes the IoT paradigm qualified. This chapter provides an overview of architecting IoT-based healthcare systems with different machine learning algorithms. This chapter elaborates the smart data characteristics and design considerations for efficient adoption of machine learning algorithms into IoT applications. In addition, various existing and hybrid classification algorithms are applied to sensory data for identifying falls from other daily activities.
Classification of Staphylococcus Aureus FabI Inhibitors by Machine Learning Techniques
Authors: Maltarollo Vinicius Gonçalves
Published: July 2022

Enoyl-acyl carrier protein reductase (FabI) is a key enzyme in the fatty acid metabolism of gram-positive bacteria and is considered a potential target for new antibacterial drugs development. Indeed, triclosan is a widely employed antibacterial and AFN-1252 is currently under phase-II clinical trials, both are known as FabI inhibitors. Nowadays, there is an urgent need for new drug discovery due to increasing antibacterial resistance. In the present study, classification models using machine learning techniques were generated to distinguish SaFabI inhibitors from non-inhibitors successfully (e.g., Mathews correlation coefficient values equal to 0.837 and 0.789 calculated with internal and external validations). The interpretation of a selected model indicates that larger compounds, number of N atoms and the distance between central amide and naphthyridinone ring are important to biological activity, corroborating previous studies. Therefore, these obtained information and generated models can be useful for design/discovery of novel bioactive ligands as potential antibacterial agents.
Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods
Authors: Yucel Melda , Kayabekir Aylin Ece , Nigdeli Sinan Melih , Bekdaş Gebrail
Published: July 2022

In this chapter, an application for demonstrating the predictive success and error performance of ensemble methods combined via various machine learning and artificial intelligence algorithms and techniques was performed. For this reason, two single methods were selected, and combination models with a Bagging ensemble were constructed and operated with the goal of optimally designing concrete beams covering with carbon-fiber-reinforced polymers (CFRP) by ensuring the determination of the design variables. The first part was an optimization problem and method composing an advanced bio-inspired metaheuristic called the Jaya algorithm. Machine learning prediction methods and their operation logics were detailed. Performance evaluations and error indicators were represented for the prediction models. In the last part, performed prediction applications and created models were introduced. Also, the obtained predictive success of the main model, as generated with optimization results, was utilized to determine the optimal predictions of the test models.
An Exploratory Study on the Use of Machine Learning to Predict Student Academic Performance
Authors: Kenekayoro Patrick
Published: July 2022

Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in computing and mathematics disciplines based on past scores in computing, mathematics and statistics subjects. Past subjects passed by students were ranked with state of art feature selection techniques in an attempt to identify any connection between good performance in a particular discipline and past subject knowledge. Up to 80% classification accuracy was achieved with support vector machines, demonstrating that this method can be developed to produce recommender or guidance systems for students, however the classification model will still benefit from more training examples. The results from this research reemphasizes the possibility and benefits of using machine learning techniques to improve teaching and learning in higher education institutions.
Thwarting Spam on Facebook
Authors: Jain Arti , Gairola Reetika , Jain Shikha , Arora Anuja
Published: July 2022

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.
Machine Learning for Smart Tourism and Retail
Authors: Rodríguez-Pardo Carlos , Patricio Miguel A. , Berlanga Antonio , Molina José M.
Published: July 2022

The unprecedented growth in the amount and variety of data we can store about the behaviour of customers has been parallel to the popularization and development of machine learning algorithms. This confluence of factors has created the opportunity of understanding customer behaviour and preferences in ways that were undreamt of in the past. In this chapter, the authors study the possibilities of different state-of-the-art machine learning algorithms for retail and smart tourism applications, which are domains that share common characteristics, such as contextual dependence and the kind of data that can be used to understand customers. They explore how supervised, unsupervised, and recommender systems can be used to profile, segment, and create value for customers.
A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing
Authors: Tianxing Man , Baimuratov Ildar Raisovich , Zhukova Natalia Alexandrovna
Published: July 2022

With the development of the Big Data, data analysis technology has been actively developed, and now it is used in various subject fields. More and more non-computer professional researchers use machine learning algorithms in their work. Unfortunately, datasets can be messy and knowledge cannot be directly extracted, which is why they need preprocessing. Because of the diversity of the algorithms, it is difficult for researchers to find the most suitable algorithm. Most of them choose algorithms through their intuition. The result is often unsatisfactory. Therefore, this article proposes a recommendation system for data processing. This system consists of an ontology subsystem and an estimation subsystem. Ontology technology is used to represent machine learning algorithm taxonomy, and information-theoretic based criteria are used to form recommendations. This system helps users to apply data processing algorithms without specific knowledge from the data science field.
Machine-Learning-Based Image Feature Selection
Authors: Verma Vivek K. , Jain Tarun
Published: July 2022

This is the age of big data where aggregating information is simple and keeping it economical. Tragically, as the measure of machine intelligible data builds, the capacity to comprehend and make utilization of it doesn't keep pace with its development. In content-based image retrieval (CBIR) applications, every database needs its comparing parameter setting for feature extraction. CBIR is the application of computer vision techniques to the image retrieval problem that is the problem of searching for digital images in large databases. In any case, the vast majority of the CBIR frameworks perform ordering by an arrangement of settled and pre-particular parameters. All the major machine-learning-based search algorithms have discussed in this chapter for better understanding related with the image retrieval accuracy. The efficiency of FS using machine learning compared with some other search algorithms and observed for the improvement of the CBIR system.
Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat
Authors: Mkrttchian Vardan , Gamidullaeva Leyla , Vertakova Yulia , Panasenko Svetlana
Published: July 2022

This chapter is devoted to studying the opportunities of machine learning with avatar-based management techniques aimed at optimizing threat for cyber security professionals. The authors of the chapter developed a triangular scheme of machine learning, which included at each vertex one participant: a trainee, training, and an expert. To realize the goal set by the authors, an intelligent agent is included in the triangular scheme. The authors developed the innovation tools using intelligent visualization techniques for big data analytic with avatar-based management in sliding mode introduced by V. Mkrttchian in his books and chapters published by IGI Global in 2017-18. The developed algorithm, in contrast to the well-known, uses a three-loop feedback system that regulates the current state of the program depending on the user's actions, virtual state, and the status of implementation of available hardware resources. The algorithm of automatic situational selection of interactive software component configuration in virtual machine learning environment in intelligent-analytic platforms was developed.
A Review of Machine Learning Methods Applied for Handling Zero-Day Attacks in the Cloud Environment
Authors: Swathy Akshaya M.  , Ganapathi Padmavathi
Published: July 2022

Cloud computing is an emerging technological paradigm that provides a flexible, scalable, and reliable infrastructure and services for organizations. Services of cloud computing is based on sharing; thus, it is open for attacker to attack on its security. The main thing that grabs the organizations to adapt the cloud computing technology is cost reduction through optimized and efficient computing, but there are various vulnerabilities and threats in cloud computing that affect its security. Providing security in such a system is a major concern as it uses public network to transmit data to a remote server. Therefore, the biggest problem of cloud computing system is its security. The objective of the chapter is to review Machine learning methods that are applied to handle zero-day attacks in a cloud environment.
Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms
Authors: Prayaga Lakshmi , Devulapalli Krishna , Prayaga Chandra
Published: July 2022

Wearable devices are contributing heavily towards the proliferation of data and creating a rich minefield for data analytics. Recent trends in the design of wearable devices include several embedded sensors which also provide useful data for many applications. This research presents results obtained from studying human-activity related data, collected from wearable devices. The activities considered for this study were working at the computer, standing and walking, standing, walking, walking up and down the stairs, and talking while walking. The research entails the use of a portion of the data to train machine learning algorithms and build a model. The rest of the data is used as test data for predicting the activity of an individual. Details of data collection, processing, and presentation are also discussed. After studying the literature and the data sets, a Random Forest machine learning algorithm was determined to be best applicable algorithm for analyzing data from wearable devices. The software used in this research includes the R statistical package and the SensorLog app.
Drug Prediction in Healthcare Using Big Data and Machine Learning
Authors: Rashid Mamoon , Goyal Vishal , Parah Shabir Ahmad , Singh Harjeet
Published: July 2022

The healthcare system is literally losing patients due to improper diagnosis, accidents, and infections in hospitals alone. To address these challenges, the authors are proposing the drug prediction model that will act as informative guide for patients and help them for taking right medicines for the cure of particular disease. In this chapter, the authors are proposing use of Hadoop distributed file system for the storage of medical datasets related to medicinal drugs. MLLib Library of Apache Spark is to be used for initial data analysis for drug suggestions related to symptoms gathered from particular user. The model will analyze the previous history of patients for any side effects of the drug to be recommended. This proposal will consider weather and maps API from Google as well so that the patients can easily locate the nearby stores where the medicines will be available. It is believed that this proposal of research will surely eradicate the issues by prescribing the optimal drug and its availability by giving the location of the retailer of that drug near the customer.
Application of Machine Learning Methods for Passenger Demand Prediction in Transfer Stations of Istanbul's Public Transportation System
Authors: Yumurtaci Aydogmus Hacer , Turkan Yusuf Sait
Published: July 2022

The rapid growth in the number of drivers and vehicles in the population and the need for easy transportation of people increases the importance of public transportation. Traffic becomes a growing problem in Istanbul which is Turkey's greatest urban settlement area. Decisions on investments and projections for the public transportation should be well planned by considering the total number of passengers and the variations in the demand on the different regions. The success of this planning is directly related to the accurate passenger demand forecasting. In this study, machine learning algorithms are tested in a real world demand forecasting problem where hourly passenger demands collected from two transfer stations of a public transportation system. The machine learning techniques are run in the WEKA software and the performance of methods are compared by MAE and RMSE statistical measures. The results show that the bagging based decision tree methods and rules methods have the best performance.
Review on Machine and Deep Learning Applications for Cyber Security
Authors: Thangavel M.  , Abiramie Shree T. G. R.  , Priyadharshini P.  , Saranya T.
Published: July 2022

In today's world, everyone is generating a large amount of data on their own. With this amount of data generation, there is a change of security compromise of our data. This leads us to extend the security needs beyond the traditional approach which emerges the field of cyber security. Cyber security's core functionality is to protect all types of information, which includes hardware and software from cyber threats. The number of threats and attacks is increasing each year with a high difference between them. Machine learning and deep learning applications can be done to this attack, reducing the complexity to solve the problem and helping us to recover very easily. The algorithms used by both approaches are support vector machine (SVM), Bayesian algorithm, deep belief network (DBN), and deep random neural network (Deep RNN). These techniques provide better results than that of the traditional approach. The companies which use this approach in the real time scenarios are also covered in this chapter.
A Machine Learning Approach for Predicting Bank Customer Behavior in the Banking Industry
Authors: Ho Siu Cheung , Wong Kin Chun , Yau Yuen Kwan , Yip Chi Kwan
Published: July 2022

Currently, Chinese commercial banks are facing extremely tremendous pressure, including financial disintermediation, interest rate marketization, and internet finance. Meanwhile, increasing financial consumption demand of customers further intensifies the competition among commercial banks. Hence, it is very important to store, process, manage, and analyze the data to extract knowledge from the customer to predict their investment direction in future. Customer retention and fraud detection are the main information for the bank to predict customer behavior. It may involve the privacy data and sensitive data of the customer. Data security and data protection for the machine learning prediction is necessary before data collection. The research is focused on two parts: the first part is data security of machine learning and second part is machine learning prediction. The result is to prove the data security for the machine learning is important. Using different machining learning analysis tool to enhance the performance and reliability of machine learning applications, the customer behavior prediction accuracy can be enhanced.
Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques
Authors: Misra Puneet , Chaurasia Siddharth
Published: July 2022

Stock market movements are affected by numerous factors making it one of the most challenging problems for forecasting. This article attempts to predict the direction of movement of stock and stock indices. The study uses three classifiers - Artificial Neural Network, Random Forest and Support Vector Machine with four different representation of inputs. First representation uses raw data (open, high, low, close and volume), The second uses ten features in the form of technical indicators generated by use of technical analysis. The third and fourth portrayal presents two different ways of converting the indicator data into discrete trend data. Experimental results suggest that for raw data support vector machine provides the best results. For other representations, there is no clear winner regarding models applied, but portrayal of data by the proposed approach gave best overall results for all the models and financial series. Consistency of the results highlight the importance of feature generation and right representation of dataset to machine learning techniques.
Analysis of Machine Learning Algorithms for Breast Cancer Detection
Authors: Aswathy M. A.  , Mohan Jagannath
Published: July 2022

As per the latest health ministry registries of 2017-2018, breast cancer among women has ranked number one in India and number two in United States. Despite the fact that breast cancer affects men also, pervasiveness is lower in men than women. This is the reason breast cancer is such a vital concern among ladies. Roughly 80% of cancer malignancies emerge from epithelial cells inside breast tissues. In breast cancer spectrum, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) are considered malignant cancers that need treatment and care. This chapter mainly deals with breast cancer and machine learning (ML) applications. All through this chapter, different issues related to breast cancer prognosis and early detection and diagnostic techniques using various ML algorithms are addressed.
The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks
Authors: Klaine Paulo Valente , Onireti Oluwakayode , Souza Richard Demo , Imran Muhammad Ali
Published: July 2022

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.
Efficacy of Networking and Collaborations
Authors: Maher Chi
Published: July 2022

Social enterprises have business models that aim to solve social, health, and environmental issues in communities. Drawing on theoretical perspectives in political science, neo-classical economists, and third sector management, the research seeks to understand organisation-level explanation of the types of networking and collaborative strategies small social enterprises employed pre-COVID-19 and post-COVID-19 lockdown. This rapid response research combines interviews with 22 third sector chief executive officers and analysis of policy measures announced during the COVID-19 lockdown. The findings suggest identifiable benefits of networking and collaboration between organisations and across sectors during the COVID-19 lockdown. The study represents an original contribution to knowledge and understanding of organisations networking and collaboration strategies employed to drive the transformation of health and social care services in communities.
Colombian Advertising Industry
Authors: Barrios Andres , Sezen Burcu
Published: July 2022

This chapter analyzes how a country context shapes advertising creativity. To do so, the chapter explores the situation of the advertising industry in Colombia (South America), a country recognized by the social focus of its advertisement. The chapter presents an overview of Colombia's socioeconomic situation, the internal conflict development, and the effects of the evolution of COVID-19. Then, a description of the country's advertising industry, media investments over the last years, and effects of the COVID-19 pandemic will follow. The chapter comprises a case study analysis employing two Colombian award-winning advertising campaigns to understand how the country's context has influenced the advertising is developed. The chapter concludes by summarizing the key learnings of the analyzed cases and by presenting suggestions on possible academic research in the country's context.
Research on Social Media Advertising in China
Authors: Yu Poshan , Liao Yuejia , Mahendran Ramya
Published: July 2022

This chapter focuses on the development of social media advertising in China, especially influencer marketing through social media posts. More specifically, this study examines how the personal traits of social media influencers, the characteristics of the content posted by them, the form of advertising, and the users' personal preferences influence the effectiveness of these influencers' advertising. Through this research study, the authors conclude that social media influencers in China act as great assets to advertising and in recent times have gained the attention from many brands. During COVID-19, social media usage increased dramatically and helped influencers increase their followership. In an ever-accelerated pace of this digital and social era, it is worthy for brands to rethink how to collaborate and partner with these influencers. It is also significant for influencers to think about how to maintain long-term relationships and garner meaningful interactions with their fans and fellow influencers.
A Study on the Prime Time Television Advertorial in India
Authors: Tam Mehmet Sinan , Babacan Kazım
Published: July 2022

Because of its dense population and rising economy, many companies in India appeal to their consumers through advertising agencies. In the Indian advertising sector, the agencies that became dominant were British agencies until 1947 and local agencies starting from 1947. When the country gained its independence in the 1990s, foreign advertising agencies again became dominant in the country with the new economic approach adopted. This research aims to explore the advertising practices in the prime-time zone on Indian television channels with a special focus on the prime-time zone advertisements of the ABP News TV channel, which broadcasts continuously on YouTube and has the most followers. In the research, it is concluded that health advertisements are broadcasted more frequently on TV than in other sectors. Performance and quality are frequently emphasized in the advertisement content, and the positioning strategy is more frequently adopted by advertising agencies.
Looking Towards the Sun
Authors: Ramsey Traci L.
Published: July 2022

Formal and informal mentorship is a valued and preferred tool among Black female higher education administrators seeking to foster connection, increase professional support, and receive direction from other experienced higher education leaders. Race can serve as a barrier to developing mentorship relationships because there are so few Black females in higher leadership roles and because cross-racial mentors may not fully understand the needs and struggles of Black females. When mentorship is lacking, professional growth and development can be stunted. This chapter examines research surrounding the needs of Black females in higher education, the value of counterspaces, and the value of cross-racial mentors. The chapter also summarizes strategies for practice when working with Black females and when serving as a mentor. Future research should determine the effectiveness of using these strategies in other minority-mentoring relationships in order to improve career advancement.