bluuebunny/crossref_metadata_2025 huggingface dataset:
Professional Learning in Hybrid Spaces
Authors: He Ye , Bagwell Dawn
Published: July 2022

To support teacher professional learning and bridge the theory-practice divide, university-school partnership has been included as one of the key elements in teacher education. Such partnerships provide opportunities to create hybrid spaces where teacher educators, administrators, pre-service teachers, in-service teachers, families, and communities share learning and development opportunities through ongoing reflections, adaptations, and inquiries. In this chapter, the authors describe the theoretical foundation and specific practices in a university-school-community partnership preparing teachers for multilingual learners. Professional learning opportunities in the hybrid spaces are detailed, and boundary-crossing engagement is discussed. Based on the partnership experiences, the authors also discussed the transformative potential of partnerships in teacher preparation to promote the development of asset-based multilingual and multicultural learning among educators, students, and communities.
Growing Your Own Teachers
Authors: Granger Karen C. , Ficklin Kelly L. , Mitchell Lisa N. , Whittington Jennifer A.
Published: July 2022

The growing teacher shortage in our P-12 schools has become a nationwide situation that universities, P-12 school systems, community colleges, and other stakeholders are addressing. Educator preparation programs have employed creative measures to recruit and retain teacher education candidates. To increase the teacher pipeline and improve retention in educator preparation programs, colleges and universities are creating pathways that are accommodating a variety of needs and meeting candidates where they are. A regional university in Southeastern North Carolina is intentionally creating opportunities for support and growth among a diverse population of teacher candidates. This chapter will explore those opportunities and share next steps and future ideas for research and development.
Increasing the Effectiveness of Novice Teachers
Authors: VanSlander Jennifer , Sharpe Sarah W. , Cardullo Victoria M.
Published: July 2022

With teacher attrition rates at an all-time high, there is a need for specialized mentoring programs and collaborative systems of support for early-career educators. Novice teachers have unique professional development needs, and their self-efficacy beliefs are highly malleable during the first few years in the classroom. Mentoring programs often focus on supports that help new teachers cope with job responsibilities but spend little time on instructional improvement and analyzing student outcomes. This conceptual research presents a collaborative professional development framework for novice teachers grounded in self-efficacy theory. The framework draws on existing empirical research on current mentoring programs and studies on the self-efficacy beliefs of novice teachers. The authors submit that vicarious and enactive mastery experience can increase novice teacher self-efficacy beliefs. The framework described subsequently has the potential to increase the instructional effectiveness of early-career educators and improve student achievement.
A Collaborative Approach Towards Mentoring and Evaluation to Support Beginning NC Pre-K Teachers Within Non-Public School Settings
Authors: Hegde Archana V. , Vestal Amanda R. , Whited Jennifer , Lambert Richard G. , Norris Ashley , Taylor Heather
Published: July 2022

In this chapter, the authors describe the importance of the early educator support (EES) program by providing a historical context and description of the work being done through this program. They further elaborate on the actual work that mentors and evaluators conduct with NC Pre-K teachers utilizing evidence- and research-based coaching and mentoring practices. Connections between the EES work and teacher induction, correlation with stages of teacher development, and a robust framework for early childhood professional development have been explained in greater detail. The chapter concludes with describing the uniqueness of the program along with challenges confronted, future implications, and its corresponding benefits.
Exploring the Impact of Instructional Coaching on Middle School Teacher Self-Efficacy
Authors: Nguyen Chau H. P. , Valle Fernando , Zimmerman Aaron S. , Almager Irma L.
Published: July 2022

This single-case study examined the impact of instructional coaching (IC) on middle school teacher self-efficacy to advance our grasp of how middle school teacher self-efficacy operates. The findings indicated that IC produces important influences on middle school teachers' self-efficacy in all the areas the principal interns (PIs) coached them. Additionally, the study found that feedback—be it praise or criticism—is perceived to be the most effective tool/component in raising teacher self-efficacy, whereas collaborating (at the beginning of the school year when trust has not yet been built) and modeling are considered the least effective. These results have implications for practice and research.
Role of Altitude in the Design of Aerospace Vehicles
Authors: S. Suthagar , K. Gopalakrishnan , T. Kumaran , Kumar Praveen
Published: July 2022

The atmosphere is the layer of gases surrounding the Earth and retained under its gravity. The atmosphere of our planet is made up of blankets of air that contain numerous gases such as oxygen, nitrogen, carbon dioxide, and others. Earth's atmosphere can be differentiated into five regions based on their physical properties such as temperature and pressure. Troposphere, stratosphere, mesosphere, ionosphere or thermosphere, and exosphere are these zones. The atmosphere provides the platform for these aerospace vehicles, which have taken off from Earth to operate at different altitudes. To investigate and understand the performance of flight tests, wind tunnel tests, and the design of aerospace vehicles or any other flying objects, there is a need for standard values to evaluate the parameters of the airplane or any other objects influenced by aerodynamic forces. Finally, the purpose of this chapter is to help the reader understand what the standard atmosphere is and how it can be utilized to analyze aeronautical vehicles.
A Review on Various Aspects of Earth's Atmosphere
Authors: D. Surekha Rathi Samundi
Published: July 2022

The atmosphere is the layer of gases that surrounds the planet. The atmosphere is retained by the gravity of the planet. Hence, it is also called planetary atmosphere. The performance of the aircraft and rockets depends on the physical properties of the atmosphere in which they fly. It is therefore advisable to study the variation of pressure, temperature, and density with the altitude. The real atmosphere is composed of dust, water vapor, and moisture, and it never remains constant. Hence, a hypothetical model called a standard atmosphere was employed. This chapter elaborates the international standard atmosphere, atmospheric boundary layer, and the stability of the atmosphere.
Supersonic Nozzle Flow
Authors: Subramani Nithya , Sekar Manigandan , Sohret Yasin , Gajapathy Gowtham
Published: July 2022

The nozzle flow is considered to be much more important in aerospace applications. Based on the pressure, velocity and temperature, flow velocity, Mach number and area, the nozzle shape will vary to meet the required condition. Nozzle is a part where the potential energy is converted into kinetic energy. The high pressure and temperature combustion product of gas is converted into high velocity and low-pressure gas as exhaust. In aviation, the main force of thrust is generated due to high velocity exhaust. This chapter gives an explanation and mathematical expression of various nozzles in both subsonic and supersonic flow along with the flow associated issues. The 3D model of convergent chevron nozzle was analyzed for the characteristics of jet mixing and acoustic effect at the exit. These results were compared with the same dimension chevron nozzle with wedges. The added wedges enhanced the jet mixing.
Aerodynamic Effectiveness of Bio-Mimic Shapes at Different Reynolds Numbers
Authors: Gajapathy G. Gowtham Gajapathy , Naranje Vishal Gangadhar , Hussein A. H. M. , R Sundharasan
Published: July 2022

Nature helps a lot to create and recreate new concepts to enhance our existence. Using nature-inspired means to address new engineering problems gives a better solution that is very quick, easy, and environmentally friendly. The goal of this chapter is to explore the overall performance increase by bioinspired design elements. Simulations of computational fluid dynamics are utilized to compare existing designs to experimental data in various flow regimes.
COVID-19 and Remote Learning in the Philippine Basic Education System
Authors: Treceñe Jasten Keneth D.
Published: July 2022

The unprecedented consequences precipitated by the COVID-19 pandemic compelled academic institutions to shift to remote education. Along with this sudden transition was the outburst of challenges with varying degrees and intensities. This chapter explored the experiences of teachers, students, and parents from rural municipalities in the Philippines who have been underrepresented in pandemic-driven investigations. Following a Husserlian phenomenological research design, participants were interviewed, and the transcripts were examined using thematic analysis. Accordingly, teachers experience various constraints to execute their duties and responsibilities. Students experience different drawbacks from learning online to mental health issues. Parents struggle to perform the role of home teachers as they are preoccupied with making a living. Although the willingness to continue education is not a question despite the present challenges, the experiences extracted in this chapter are evidence that necessitates further empirical research in the era of online education.
Fighting Through COVID-19 for Educational Continuity
Authors: Lin Edward C. P. , Yeh Andy J.
Published: July 2022

The long-term worldwide COVID-19 pandemic has caused unprecedented disruptions to regular school operations around the world, and online education has become the temporary solution from primary to higher education. Since 2020, researchers have learned that this situation has instigated various challenges to teachers, who have been on the front line fighting to continue education. However, there is not yet a global investigation from which to derive generalizable solutions that could contribute to ensuring educational continuity during school closures. Consequently, this chapter investigated the challenges teachers have faced during the pandemic by analyzing research studies from around the world. Using the method of literature review and the framework of thematic analysis, the authors identified practical solutions based on firsthand insights from 15,054 teachers. The chapter summarized eight types of major challenges to teachers in three broad categories and presented three recommendations for a more crisis-prepared education community.
Advancing Equity in Digital Classrooms
Authors: Iyer Lakshmi Shankar , Bharadwaj Sonika , Shetty Shilpa H. , Verma Vertika , Devanathan Malmarugan
Published: July 2022

Since the introduction of technology-enabled education systems, personalizing the learning process has become more regarded as a promising methodology for revolutionizing the academe. Acknowledging the difference in the learning capability of students across various levels of the academic segment, a personalized learning approach is of paramount importance, especially when teachers cannot efficiently monitor each student (e.g., during emergency remote education). This chapter focused on the necessity for higher education institutions that offer courses from various streams to adopt a personalized learning initiative as a means of offering better online education services. For the successful creation of a personalized online learning experience, this chapter likewise developed a framework that provides a step-by-step guide to educational institutions in moving in this direction. As online education is a trend for future learning, this blueprint could be valuable as well in the post-pandemic era.
Experiential Value and Experiential Quality
Authors: Çakır Fatma
Published: July 2022

The importance of providing an experience that will make the customer feel special is better understood by businesses today. It is essential for businesses to consider customer experiences, experiential value, and experiential quality with a strategic management approach focused on market success, target profitability, and global brand image. In this chapter, the concepts of experiential value, experiential quality, and the relationship between experiential value and experiential quality are examined. The chapter aims to reveal the role and importance of experiential value and experiential quality in achieving a competitive advantage for businesses. Studies in the literature show that experiential value and experiential quality reveal that it is effective in consumer decisions such as customer satisfaction, repurchase, and recommendation. Hence, businesses need to determine their marketing strategies by taking into account what kind of experience their customers want to have with their products and services.
Customer Online Shopping Experience
Authors: Durukal Esma
Published: July 2022

In the 21st century, online shopping has grown heavily, with the global online commerce volume increasing significantly due to the COVID-19 crisis. The rapid development of technology has greatly changed the way customers shop. The transition of the customer shopping model from physical stores to online shopping has led many companies to create effective online systems to retain their existing customers and acquire new customers. With the internet becoming the main channel for product and service delivery, the online shopping experience has become vital. This study covers how the latest and notable technological innovations are shaping the customer online shopping experience, advancing customer adoption, and e-commerce success. Online customer experience is an extremely important issue for companies today. In this context, the internet of things (IoT), artificial intelligence (AI), augmented reality (AR), virtual reality (VR), blockchain, and metaverse technologies play an important role in the success of online customer experience.
Creating Brand Experience Based on Brand Equity in Tourism Destinations
Authors: Küçükkambak Selçuk Efe
Published: July 2022

The purpose of introducing the attractions of destinations, making them known to tourists, determining the brand elements specific to the holiday region, and creating associations in the consumer over time is to attract a certain number of visitors to the holiday destination every year. The aim of local governments in tourism destinations, associations or organizations that carry out tourism and promotion activities, and tourism operators is to attract a certain number of tourists to the region every year, to provide them with a good holiday experience, to satisfy them during their stay, and to create an attitude towards revisiting the area. The most important achievement in this process is to develop loyal tourists to the tourism destination. This chapter examined the importance of brand loyalty in destinations based on brand management principles and experiential marketing. By giving place to the results of various researches carried out in this field in the world, gaps in the literature have been identified, and it is aimed to make inferences that will guide future studies.
The Changing Face of the Tour Guide and Its Impact on Tourist Experiences
Authors: Şengöz Ayşe
Published: July 2022

Tourism is an important sector due to its contribution to the economy and employment. Despite the decreasing tourism activities under pandemic conditions, positive effects can be mentioned for all countries of the world. While tourism has an effect on all stakeholders, time also has various effects on tourism. Tour guides who try to act in accordance with these effects have to improve and transform themselves. A tour guide who can keep up with innovations can only benefit his country and provide satisfaction to the customer. Therefore, in this chapter, the development and usability of information and communication technologies, socio-demographic trends, the changing profiles and preferences of tourists, the expectations of tourists regarding social and environmental responsibility, and the implications and reactions of the guided tour industry will be examined. Moreover, escaping from overcrowded destinations, guides, and tourists who are prone to smart tourism, the changing nature of tour guide communication, and changing travel models and approaches will be examined in detail.
Tourism Experience Management and Marketing in Gastronomy
Authors: Karaca Şükran , Baran Zuleyhan
Published: July 2022

The tourism experience can drive the behavior of the contemporary tourist who travels to experience unique, extraordinary destinations and events. The concepts of tourism and hospitality are often described as a concept related to gastronomy, which is closely related to culture. Gastronomy tourism has become an important concept in attracting tourists to specific destinations and offering tourists interesting, new, and unusual experiences with the spread of global travel in the 20th century. Therefore, more attention is paid to the behavioral consequences of this situation for tourists and their role in tourism and accommodation marketing and branding. In this context, experiential marketing and management issues in tourism and gastronomy are examined in this chapter. Thus, by providing a better understanding of tourism experiences, some suggestions have been presented to destination marketers and managers and businesses that provide tourism services.
The Phygital Experiential Marketing Practices
Authors: Akmermer Bilgen
Published: July 2022

Over the past decade, the swift pace of digitalization and the pressure of new era technologies have not only transformed the perception and expectations of new generations but also completely created a new world of experiences. Today, the new generations are ready to experience a hybrid world described as phygital that combines digital and physical spaces. This new phygital world also provides unique value for the tourism industry to establish a new channel of interactivity with its consumers. This chapter examines the phygital concept and its impact on tourism experiences and tourism destination management. Therefore, a conceptual framework is given to deeply understand the state of the phygital. Firstly, the chapter explores augmented reality and virtual reality technologies triggering this concept, and afterward, the chapter cites the phygital-based experiential marketing practices for tourism pioneers.
Applications of Deep Learning in Agriculture
Authors: Tripathi Padmesh , Kumar Nitendra , Rai Mritunjay , Khan Ayoub
Published: July 2022

Today's era is the era of technologies. Technologies have widely been employed in each and every field. The field of agriculture is not untouched with the technologies, and in several segments of agriculture; it has been employed at large. Deep learning techniques and its variants like convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial network (AGN), and their various subcategories like AlexNet, ImageNet, visual geometry group (VGG), etc. have widely been employed in many sectors of agriculture in order to increase the quality and quantity of production. In this chapter, some applications of deep learning have been explored.
A Sustainable Approach of Artificial Neural Network for Prediction of Irrigation, Pesticides, Fertilizers, and Crop Yield
Authors: Prakash Pandurangan , Elanthendral G. , Vedanarayanan V. , Aravindh Kumar J. , Samrot Antony V. , Jaiganesh R.
Published: July 2022

Food demand increases as the world's population expands; the world populace will reach 9.9 billion by 2050. India will be the world's largest population by 2024. Agriculture, therefore, should be fruitful and affordable for subsistence. Organic methods of agriculture are still effective for healthier crops. However, production shrinks as it depends wholly on manual labour. Since traditional farming, agriculture has seen various revolutions and developments. Currently, in the era of Agriculture 5.0, precision agriculture principles using artificial intelligence, machine learning, and IoT are being used. India still relies heavily on manual work. Educational level, inadequate training, and indigent farmers put India at a disadvantage. Technology may lead to sustainable agriculture, which means integration of plant and animal production that leaves unshakable benefits on the environment, farmers, and society that essential for the climate change- and disaster-prone world. Machine learning techniques that can possibly cater to various agricultural challenges faced by famers in India are reviewed.
Impact of Artificial Intelligence and Machine Learning in the Food Industry
Authors: Sharma Archana , Mittal Kajol , Kumar Sunil , Sharma Utkarsh , Upadhyay Prashant
Published: July 2022

In recent years, the food sector or industry has escalated to prominence as the most important industry to receive widespread attention. It encompasses various industrial activities related to food production, distribution, processing, preparation, preservation, transportation, and packaging. Machine learning (ML) is a subpart of artificial intelligence (AI), and it is widely used in the food sector for industrial automation and predictive modeling with the world's growing demand and population. AI assists in improving package shelf life, menu selection, food cleanliness, and safety. Because of AI and machine learning, smart agriculture, drones, and robotics in the area of the food sector are becoming the need of the modern era. This chapter discusses how AI and machine learning have the potential to be used in the food business to save money while simultaneously increasing resource efficiency. It highlights the food industry's achievements and challenges with specific attention to the role of machine learning and artificial intelligence.
High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods
Authors: Yucel Melda , Namlı Ersin
Published: July 2022

In this chapter, the authors realized prediction applications of concrete compressive strength values via generation of various hybrid models, which are based on decision trees as main a prediction method. This was completed by using different artificial intelligence and machine learning techniques. In respect to this aim, the authors presented a literature review. The authors explained the machine learning methods that they used as well as with their developments and structural features. Next, the authors performed various applications to predict concrete compressive strength. Then, the feature selection was applied to a prediction model in order to determine parameters that were primarily important for the compressive strength prediction model. The authors evaluated the success of both models with respect to correctness and precision prediction of values with different error metrics and calculations.
Modern Statistical Modeling in Machine Learning and Big Data Analytics
Authors: Ramezani Niloofar
Published: July 2022

Machine learning, big data, and high dimensional data are the topics we hear about frequently these days, and some even call them the wave of the future. Therefore, it is important to use appropriate statistical models, which have been established for many years, and their efficiency has already been evaluated to contribute into advancing machine learning, which is a relatively newer field of study. Different algorithms that can be used within machine learning, depending on the nature of the variables, are discussed, and appropriate statistical techniques for modeling them are presented in this chapter.
Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods
Authors: Narayan Valliammal , Shaju Barani
Published: July 2022

This chapter aims to discuss applications of machine learning in cyber security and explore how machine learning algorithms help to fight cyber-attacks. Cyber-attacks are wide and varied in multiple forms. The key benefit of machine learning algorithms is that it can deep dive and analyze system behavior and identify anomalies which do not correlate with expected behavior. Algorithms can be trained to observe multiple data sets and strategize payload beforehand in detection of malware analysis.
Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks
Authors: Ghosh Monalisa , Singhal Chetna
Published: July 2022

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.
A Fast Feature Selection Method Based on Coefficient of Variation for Diabetics Prediction Using Machine Learning
Authors: Li Tengyue , Fong Simon
Published: July 2022

Diabetes has become a prevalent metabolic disease nowadays, affecting patients of all age groups and large populations around the world. Early detection would facilitate early treatment that helps the prognosis. In the literature of computational intelligence and medical care communities, different techniques have been proposed in predicting diabetes based on the historical records of related symptoms. The researchers share a common goal of improving the accuracy of a diabetes prediction model. In addition to the model induction algorithms, feature selection is a significant approach in retaining only the relevant attributes for the sake of building a quality prediction model later. In this article, a novel and simple feature selection criterion called Coefficient of Variation (CV) is proposed as a filter-based feature selection scheme. By following the CV method, attributes that have a data dispersion too low are disqualified from the model construction process. Thereby the attributes which are factors leading to poor model accuracy are discarded. The computation of CV is simple, hence enabling an efficient feature selection process. Computer simulation experiments by using the Prima Indian diabetes dataset is used to compare the performance of CV with other traditional feature selection methods. Superior results by CV are observed.
Development of a Classification Model for CD4 Count of HIV Patients Using Supervised Machine Learning Algorithms
Authors: Idowu Peter Adebayo , Balogun Jeremiah Ademola
Published: July 2022

This chapter was developed with a view to present a predictive model for the classification of the level of CD4 count of HIV patients receiving ART/HAART treatment in Nigeria. Following the review of literature, the pre-determining factors for determining CD4 count were identified and validated by experts while historical data explaining the relationship between the factors and CD4 count level was collected. The predictive model for CD4 count level was formulated using C4.5 decision trees (DT), support vector machines (SVM), and the multi-layer perceptron (MLP) classifiers based on the identified factors which were formulated using WEKA software and validated. The results showed that decision trees algorithm revealed five (5) important variables, namely age group, white blood cell count, viral load, time of diagnosing HIV, and age of the patient. The MLP had the best performance with a value of 100% followed by the SVM with an accuracy of 91.1%, and both were observed to outperform the DT algorithm used.
An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine
Authors: Sousa Lucas S , Filho Pedro P Rebouças , Bezerra Francisco Nivando , Neto Ajalmar R Rocha , Oliveira Saulo A. F.
Published: July 2022

Retinal images are commonly used to diagnose various diseases, such as diabetic retinopathy, glaucoma, and hypertension. An important step in the analysis of such images is the detection of blood vessels, which is usually done manually and is time consuming. The main proposal in this work is a fast method for retinal blood vessel detection using Extreme Learning Machine (ELM). ELM requires only one iteration to complete its training and it is a robust and fast network in all aspects. The proposal is a compact and efficient representation of retinal images in which the authors achieved a reduction up to 39% of the initial data volume, while still keeping representativeness. To achieve such a reduction whilst maintaining the representativeness, three features (local tophat, local average, and local variance) were used. According to the simulations carried out, this proposal achieved an accuracy of about 95% for most results, outperforming most of the state-of-art methods. Furthermore, this proposal has greater sensitivity, meaning that more vessel pixels are detected correctly.
Machine Learning Based Taxonomy and Analysis of English Learners' Translation Errors
Authors: Qin Ying
Published: July 2022

This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types Chinese to English (C2E) translation errors are finally confirmed. There probably exists three categories of top-level errors according to the hierarchical clustering results. In addition, three supervised learning methods are applied to automatically recognize the types of errors, among which the highest performance reaches F1 = 0.85 on E2C and F1 = 0.90 on C2E translation. Further comparison to the intuitive or theoretical studies on translation taxonomy shows some phenomenon accompanied by language skill improvement of Chinese learners. Analysis on translation problems based on machine learning provides the objective insight and understanding on the students' translations.
Concerning the Integration of Machine Learning Content in Mechatronics Curricula
Authors: Frochte Jörg , Lemmen Markus , Schmidt Marco
Published: July 2022

Machine learning is becoming more and more important for mechatronic systems and will become an ordinary part of today's student life. Thus, it is obvious that machine learning should be part of today's student's curriculum. Unfortunately, machine learning seldomly is implemented into the curriculum in a substantial or linking manner, but rather offered as an elective course. This chapter provides an analysis of how machine learning can be integrated as a mandatory part of the curriculum of mechatronic degree courses. It is considered what the required minimal changes in fundamental courses should be and how traditional subjects like robotics, automation, and automotive engineering can profit most of this approach. As a case study, this chapter utilizes an existing German mechatronic degree course specialized on information technology, which covers most of the discussed aspects.
A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods
Authors: Rodríguez Jhonn Pablo , Corrales David Camilo , Corrales Juan Carlos
Published: July 2022

This article describes how coffee rust has become a serious concern for many coffee farmers and manufacturers. The American Phytopathological Society discusses its importance saying this: “…the most economically important coffee disease in the world…” while “…in monetary value, coffee is the most important agricultural product in international trade…” The early detection has inspired researchers to apply supervised learning algorithms on predicting the disease appearance. However, the main issue of the related works is the small number of samples of the dependent variable: Incidence Percentage of Rust, since the datasets do not have a reliable representation of the disease, which will generate inaccurate predictions in the models. This article provides a process about coffee rust to select appropriate machine learning methods to increase rust samples.
Machine Learning, Data Mining for IoT-Based Systems
Authors: Kashyap Ramgopal
Published: July 2022

This chapter will addresses challenges with the internet of things (IoT) and machine learning (ML), how a bit of the trouble of machine learning executions are recorded here and should be recalled while arranging the game plan, and the decision of right figuring. Existing examination in ML and IoT was centered around discovering how garbage in will convey garbage out, which is extraordinarily suitable for the extent of the enlightening list for machine learning. The quality, aggregate, availability, and decision of data are essential to the accomplishment of a machine learning game plan. Therefore, the point of this section is to give an outline of how the framework can utilize advancements alongside machine learning and difficulties get a kick out of the chance to understand the security challenges IoT can be bolstered. There are a few extensively unmistakable counts open for ML use. In spite of the way that counts can work in any nonexclusive conditions, there are specific standards available about which figuring would work best under which conditions.
Object Detection in Fog Computing Using Machine Learning Algorithms
Authors: Bhargavi Peyakunta , Jyothi Singaraju
Published: July 2022

The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.
Genetic Programming as Supervised Machine Learning Algorithm
Authors: Hrnjica Bahrudin , Mehr Ali Danandeh
Published: July 2022

This chapter presents the theory and procedures behind supervised machine learning and how genetic programming can be applied to be an effective machine learning algorithm. Due to simple and powerful concept of computer programs, genetic programming can solve many supervised machine learning problems, especially regression and classifications. The chapter starts with theory of supervised machine learning by describing the three main groups of modelling: regression, binary, and multiclass classification. Through those kinds of modelling, the most important performance parameters and skill scores are introduced. The chapter also describes procedures of the model evaluation and construction of confusion matrix for binary and multiclass classification. The second part describes in detail how to use genetic programming in order to build high performance GP models for regression and classifications. It also describes the procedure of generating computer programs for binary and multiclass calcification problems by introducing the concept of predefined root node.
Programming Language Support for Implementing Machine Learning Algorithms
Authors: Anitha Elavarasi S.  , Jayanthi J.
Published: July 2022

Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.
A Heterogeneous AdaBoost Ensemble Based Extreme Learning Machines for Imbalanced Data
Authors: Abuassba Adnan Omer , Zhang Dezheng , Luo Xiong
Published: July 2022

Extreme learning machine (ELM) is an effective learning algorithm for the single hidden layer feed-forward neural network (SLFN). It is diversified in the form of kernels or feature mapping functions, while achieving a good learning performance. It is agile in learning and often has good performance, including kernel ELM and Regularized ELM. Dealing with imbalanced data has been a long-term focus for the learning algorithms to achieve satisfactory analytical results. It is obvious that the unbalanced class distribution imposes very challenging obstacles to implement learning tasks in real-world applications, including online visual tracking and image quality assessment. This article addresses this issue through advanced diverse AdaBoost based ELM ensemble (AELME) for imbalanced binary and multiclass data classification. This article aims to improve classification accuracy of the imbalanced data. In the proposed method, the ensemble is developed while splitting the trained data into corresponding subsets. And different algorithms of enhanced ELM, including regularized ELM and kernel ELM, are used as base learners, so that an active learner is constructed from a group of relatively weak base learners. Furthermore, AELME is implemented by training a randomly selected ELM classifier on a subset, chosen by random re-sampling. Then, the labels of unseen data could be predicted using the weighting approach. AELME is validated through classification on real-world benchmark datasets.
Advanced-Level Security in Network and Real-Time Applications Using Machine Learning Approaches
Authors: Rath Mamata , Mishra Sushruta
Published: July 2022

Machine learning is a field that is developed out of artificial intelligence (AI). Applying AI, we needed to manufacture better and keen machines. Be that as it may, aside from a couple of simple errands, for example, finding the briefest way between two points, it isn't to program more mind boggling and continually developing difficulties. There was an acknowledgment that the best way to have the capacity to accomplish this undertaking was to give machines a chance to gain from itself. This sounds like a youngster learning from itself. So, machine learning was produced as another capacity for computers. Also, machine learning is available in such huge numbers of sections of technology that we don't understand it while utilizing it. This chapter explores advanced-level security in network and real-time applications using machine learning.
Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method
Authors: Li Hao , Liu Zhijian
Published: July 2022

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost, and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this chapter, the authors show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This chapter consists of: 1) comparative studies on varieties of machine learning models (artificial neural networks [ANNs], support vector machine [SVM], and extreme learning machine [ELM]) to predict the performances of SWHs; 2) development of an ANN-based software to assist the quick prediction; and 3) introduction of a computational HTS method to design a high-performance SWH system.
Credit Rating Forecasting Using Machine Learning Techniques
Authors: Wallis Mark , Kumar Kuldeep , Gepp Adrian
Published: July 2022

Credit ratings are an important metric for business managers and a contributor to economic growth. Forecasting such ratings might be a suitable application of big data analytics. As machine learning is one of the foundations of intelligent big data analytics, this chapter presents a comparative analysis of traditional statistical models and popular machine learning models for the prediction of Moody's long-term corporate debt ratings. Machine learning techniques such as artificial neural networks, support vector machines, and random forests generally outperformed their traditional counterparts in terms of both overall accuracy and the Kappa statistic. The parametric models may be hindered by missing variables and restrictive assumptions about the underlying distributions in the data. This chapter reveals the relative effectiveness of non-parametric big data analytics to model a complex process that frequently arises in business, specifically determining credit ratings.
Cyber Secure Man-in-the-Middle Attack Intrusion Detection Using Machine Learning Algorithms
Authors: Natarajan Jayapandian
Published: July 2022

The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission.
A Comparative Review of Various Machine Learning Approaches for Improving the Performance of Stego Anomaly Detection
Authors: Jeyaprakash Hemalatha , M. K. KavithaDevi , S. Geetha
Published: July 2022

In recent years, steganalyzers are intelligently detecting the stego images with high detection rate using high dimensional cover representation. And so the steganographers are working towards this issue to protect the cover element dependency and to protect the detection of hiding secret messages. Any steganalysis algorithm may achieve its success in two ways: 1) extracting the most sensitive features to expose the footprints of message hiding; 2) designing or building an effective classifier engine to favorably detect the stego images through learning all the stego sensitive features. In this chapter, the authors improve the stego anomaly detection using the second approach. This chapter presents a comparative review of application of the machine learning tools for steganalysis problem and recommends the best classifier that produces a superior detection rate.
Smart Pollution Alert System Using Machine Learning
Authors: Chitra P. , Abirami S.
Published: July 2022

This chapter proposes a novel mobile-based pollution alert system. The level of the pollutants is available in the air quality repository. This data is updated periodically by collecting the information from the sensors placed at the monitoring stations of different regions. A model using artificial neural network (ANN) is proposed to predict the AQI values based on the present and previous values of the pollutants. The ANN model processes the normalized data and predicts whether the region is hazardous or not. A novel mobile application which could be used by the user to know about the present and future pollution level could be developed using a progressive web application development environment. This mobile application uses the location information of the user and helps the user to predict the hazardous level of the pollutants in that particular location.
Classification of Traffic Events in Mexico City Using Machine Learning and Volunteered Geographic Information
Authors: Saldana-Perez Magdalena , Torres-Ruiz Miguel , Moreno-Ibarra Marco
Published: July 2022

Volunteer geographic information and user-generated content represents a source of updated information about what people perceive from their environment. Its analysis generates the opportunity to develop processes to study and solve social problems that affect the people's lives, merging technology and real data. One of the problems in urban areas is the traffic. Every day at big cities people lose time, money, and life quality when they get stuck in traffic jams; another urban problem derived from traffic is air pollution. In the present approach, a traffic event classification methodology is implemented to analyze VGI and internet information related to traffic events with a view to identify the main traffic problems in a city and to visualize the congested roads. The methodology uses different computing tools and algorithms to achieve the goal. To obtain the data, a social media and RSS channels are consulted. The extracted data texts are classified into seven possible traffic events, and geolocalized. In the classification, a machine learning algorithm is applied.
Adoption of Machine Learning With Adaptive Approach for Securing CPS
Authors: Sigamani Rama Mercy Sam
Published: July 2022

The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence algorithms have been proposed to detect and mitigate the cyber-attacks in cyber physical systems, smart grids, power systems. The various machine learning approaches towards securing CPS is observed based on the performance metrics like detection accuracy, average classification rate, false negative rate, false positive rate, processing time per packet. A unique feature of CPS is considered through structural adaptation which facilitates a self-healing CPS.