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Recruitment Analytics: Hiring in the Era of Artificial Intelligence
Introduction: Traditional recruitment system relied heavily on the applicants curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be misfits. CVs were the only source of candidates data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process. Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages. Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes. Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles. Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidates perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees queries. Resume screening techniques can save the recruiters time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team. 2023 by V. R. Uma, Ilango Velchamy and Deepika Upadhyay. -
Insuretech: Saviour of insurance sector in India
Technology in finance has propelled financial literacy and inclusiveness and may give the insurance sector an edge to reach its potential consumers. The current study aimed to identify the role of Fintech in transforming the insurance sector and improving the penetration rate in India. With the descriptive research design, the study collects the primary data through a survey technique targeting the general public and personnel in the insurance sector as a study population. A conceptual model is proposed to understand the interlink between consumer attitude towards Insurance, factors influencing their decision, and the role of Fintech in bridging the gap in insurance penetration. This study focuses on three areas, namely health insurance, life insurance, and vehicle insurance. The study's findings reveal that the insurtech will significantly improve the efficiency of the insurance sector which will result in significant financial performance. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Waste Management for Waste Entrepreneurship: An Emerging Concept
Since the very beginning of civilization, waste has always been an incessant problem and their management remains burdensome till date, as the rate of waste generation is increasing with the increase in population, land use and development of economy. Waste is generally considered as an unavoidable trash/nuisance with zero value and concerns which can be overruled by the waste management system. It is a well-organized holistic expensive process that includes segregation at sources, on-time collection, transportation, reuse, recycle, reprocess and disposal of the leftover materials into the landfills, which usually receives inadequate attention as public get easily acclimatized to live along with the generated wastes. Managing waste in an environmentally favorable, culturally acceptable and techno-economically feasible manner is a need in recent times. Society is in a need to think of ways to minimize and utilize waste for other uses. Understanding waste management in terms of its challenges involves knowledge dissemination to the public, waste prevention, valorization, responsible material production and packaging, maximum recycling, conservation of resources, enhancement of sustainability and reduction of greenhouse gasses. Opportunities in waste management could be achieved by exercising circular economy practices which reinforce environmental, societal and economic benefits. Role of entrepreneurs in the waste management system encompasses a cluster of skilled as well as unskilled workers, as it is a labor-intensive system. Entrepreneurs may invest money as well as infuse novel skills and technologies to transform trash into treasures. The efficacy and significance of waste management will eventually increase with the active participation of entrepreneurs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Microbial Fuel Cells: The Microbial Route for Bioelectricity
The quest for sustainable energy sources serves as the essential pillar for development of humans since the dawn of civilization. The alarming increase in demand of energy, especially electricity propelled the need to screen for alternative sources of energy over the conventional fossil based non-renewable counterparts. Electricity generation through microbial route functions by the fundamental phenomena of electron transport chain and the microbes operate as the source of energy production utilizing the substrate. Since its initiation, microbial fuel cell has gained a lot of research focus from all over the world. The integration of waste treatment with power generation was highlighted as the most productive and sustainable part of microbial fuel cells. Over the past few decades, a lot of research and development was done on improving the design of fuel cells, searching for cost-effective electrodes and membranes for commercialization. Despite tremendous research done on this domain, its commercialization still faces a lot of hurdles especially once it comes to the overall maintenance and production cost. This chapter summarizes the basic architecture of different microbial fuel cells and the challenges that need to be addressed for making microbial fuel cells a sustainable route for the bioelectricity generation from microorganisms. Springer Nature Singapore Pte Ltd. 2020. -
Fruit Waste as Sustainable Resources for Polyhydroxyalkanoate (PHA) Production
Production of polyhydroxyalkanoate (PHA) using commercially available carbon sources like glucose or sucrose makes the bioprocess economically nonviable, thereby hindering its commercialization. As an alternative to this issue, inexpensive and easily available agro-industrial wastes are now being exploited as feedstock for PHA production. Fruit wastes are generally discarded as they are considered to be the non-product leftovers which do not have any economic value when compared with the cost of their collection and recovery steps for reuse. But through the use of appropriate technological applications, these wastes can be converted to valuable by-products, which can increase the value of the products much higher than the cost associated with recovery steps. By recycling and reprocessing the fruit wastes, they can be channeled into many applications, and thereby the amount of fruit wastes discharged into the environment can be completely reduced along with their detrimental effects. Large amounts of fruit wastes are produced by fruit-based industries. The waste products can be both solids and liquids, and these wastes are of high nutritional and biomass values for microorganism; thus their addition to waterbodies can make them highly polluted (high BOD or COD). These fruit-based wastes still have a promising potential for bioconversion into products of commercial importance or can be successfully exploited as cheap raw materials for industrial production of commercially important metabolites. This chapter deals with the strategies for production of PHA from fruit waste substrates, extraction and characterization of PHA, and their applications in diverse sectors. The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021. -
Sustainable Biodegradable and Bio-based Materials
The quest for sustainable biodegradable and bio-based materials is ever increasing due to their versatile properties and also their ability to serve as potential alternatives to their synthetic counterparts. The major types of bio-based materials of commercial importance can be derived majorly from plant, animal, and microbial sources through physical, chemical, or biological extraction methods. Despite their potential applications, biocompatibility, and biodegradability, these bio-based polymers still face hurdles in competing with conventional plastics. The major factors contributing to this involve the production and extraction cost. In recent years, the integration of waste valorization with biopolymer production and the development of eco-friendly green extraction protocols with minimum usage of chemicals were visualized as efficient strategies for the sustainable production of biopolymers. This study summarizes the important biodegradable and bio-based materials of commercial importance along with their production methods and application in diverse sectors. 2023 selection and editorial matter, Ajay, Parveen, Sharif Ahmad, Jyotsna Sharma, Victor Gambhir. -
Active Learning from an Imbalanced Dataset: A Study Conducted on the Depression, Anxiety, and Stress Dataset
The proposed chapter deals with psychological data related to depression, anxiety, and stress to study how the classification and analysis is carried out on imbalanced data. The proposed study not only contributes on providing practical information about the balancing techniques such as synthetic minority oversampling technique but also reveals the strategy for dealing with the working of many existing classification algorithms such as the support vector machine, random forest, XGBoost, etc. on the imbalanced dataset. The present use of evaluation metrics that are solely implied for the imbalanced data classification is also illustrated. It was observed that the ordinary model assessment techniques do not precisely quantify model execution when gone up against imbalanced datasets and that the common techniques such as the logistic regression and decision tree have a predisposition toward classes that have many observations. The attributes of the minority class are treated low and are routinely overlooked. Henceforth, there is a high likelihood of misclassification of the minority class when compared to the majority class. A confusion matrix which contains data about the real and predicted class is used as an assessment standard to check the exhibition of grouping calculation. Rather than going for accuracy, F-score and the area under the curve are considered as the measures to evaluate the classification model. 2022 selection and editorial matter, Vishal Jain, Sapna Juneja, Abhinav Juneja, and Ramani Kannan. -
An Empirical Study ofSignal Transformation Techniques onEpileptic Seizures Using EEG Data
Signal processing may be a mathematical approach to manipulate the signals for varied applications. A mathematical relation that changes the signal from one kind to a different is named a transformation technique in the signal process. Digital processing of electroencephalography (EEG) signals plays a significant role in a highly multiple application, e.g., seizure detection, prediction, and classification. In these applications, the transformation techniques play an essential role. Signal transformation techniques are acquainted with improved transmission, storage potency, and subjective quality and collectively emphasize or discover components of interest in an extremely measured EEG signal.The transformed signals result in better classification. This article provides a study on some of the important techniques used for transformation of EEG data. During this work, we have studied six signal transformation techniques like linear regression, logistic regression, discrete wavelet transform, wavelet transform, fast Fourier transform, and principal component analysis with Eigen vector to envision their impact on the classification of epileptic seizures. Linear regression, logistic regression, and discrete wavelet transform provides high accuracy of 100%, and wavelet transform produced an accuracy of 96.35%. The proposed work is an empirical study whose main aim is to discuss some typical EEG signal transformation methods, examine their performances for epileptic seizure prediction, and eventually recommend the foremost acceptable technique for signal transformation supported by the performance. This work also highlights the advantages and disadvantages of all seven transformation techniques providing a precise comparative analysis in conjunction with the accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A meta-heuristic based hybrid predictive model for sensor network data
Many prediction algorithms and techniques are used in data mining to predict the outcome of the response variable with respect to the values of input variables. However from literature, it is confirmed that a hybrid approach is always better in performance than a single algorithm. This is because the hybridization leads to combine all the advantages of the individual approaches, leading to the production of more effective and much improved results. Thus, making the model a productive one, which is far better than model proposed using individual techniques or algorithms. The purpose behind this chapter is to provide information to the users on how to build and investigate a hybrid Feed-forward Neural Network (FNN) using nature inspired meta heuristic algorithms such as the Gravitational Search Algorithm (GSA), Binary Bat Algorithm (BBAT), and hybrid BBATGSA algorithm for the prediction of sensor network data. Here, FNN is trained using a hybrid BBATGSA algorithm for predicting temperature data in sensor network. The data is collected using 54 sensors in a controlled environment of Intel Berkeley Research lab. The developed predictive model is evaluated by comparing it with existing two meta heuristic models such as FNNGSA and FNNBBAT. Each model is tested with three different V-shaped transfer functions. The experimental results and comparative study reveal that the developed FNNBBATGSA shows best performance in terms of accuracy. The FNNBBATGSA under three different V-shaped transfer functions produced an accuracy of 91.1, 98.5, and 91.2%. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Clustering Faculty Members fortheBetterment ofResearch Outcomes: A Fuzzy Multi-criteria Decision-Making Approach inTeam Formation
From a talent-pool of people, choosing an efficient team is tough. Faculty members of a higher education institution constitute the talent-pool. Teams have to be formed from them so that research output of each team is maximum. Amongst numerous research skills, thirteen are identified as most desirable skills. The level of these thirteen skills, viz., concept articulation, formatting according to templates/style sheets, identifying the relevant literature, initiative, logical reasoning, patience, problem formulation/problem finding, proof reading skills/identifying mistakes in written communication, searching/browsing skills/quick search techniques, sense of positive criticism, statistical knowledge, the ability to stay calm, and written communication skills, varies from person to person. Historical ranking of these skills and self-evaluation of the level of acquisition of these skills is used along with the years of experience, educational qualification, gender, marital status, etc., to rank individual faculty members. The fuzzy ranking of the faculty members thus obtained is used to cluster them into teams that are efficient in complementary skills. Each team thus formed is involved in collaborative research leading to research publication. The model is successfully implemented in a university department with 40 faculty members. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A decade survey on internet of things in agriculture
The Internet of Things (IoT) is a united system comprising of physical devices, mechanical and digital machines, and different hardware components like sensors, actuators, cameras etc., monitored and operated by the software. The combination of devices and systems connected over the internet opens the pathway for development of various applications beneficial in terms of economic growth of a nation. IoT has evolved as a potentially emerging computer technology solving various real-life problems and issues. IoT covers vast group of applications, from warfare to surveillance, from habitat monitoring to energy harnessing, predictive analytics and personalized health care, and so on. Among various fields, agriculture is one important field having maximum scope of implementation and investment. The main aim of this book chapter is to furnish all the details related to applications of IoT in the field of agriculture. This includes the details related to data collection, types of sensors used, deployment details, data access through cloud. It also covers details related to various communication technologies used in IoT such as Bluetooth, LoRaWAN, LTE, 6LowPAN, NFC, RFID etc. And above all, the chapter focuses on the significance of IoT on agronomics, agricultural engineering, crop production and livestock production. This chapter is a decade survey conducted to study the contribution of IoT in the field of agriculture. Around 40 research papers for the duration 2008-2018 are collected from peer reviewed journals and conferences. The collected articles are analyzed to provide relevant information required for the various end users. Springer Nature Switzerland AG 2020. -
Deep neural network architecture and applications in healthcare
Gaining insights related to medical data has always been a challenge, as limited technology delays treatment. Various types of data are collected from the medical field, such as sensor data, that are heterogeneous in nature. All of these are very poorly maintained and require more structuring. For this reason, deep learning is becoming more and more popular in this area. There are many challenges due to inadequate and irrelevant data. Insufficient domain knowledge also adds to the challenge. Modern deep learning models can help understand the dataset. This chapter provides an overview of deep learning, its various architectures, and convolutional neural networks. It also highlights how deep learning technologies can help advance healthcare. 2022 River Publishers. -
Robo-revolution: How automated financial advisors are reshaping global finance
Robo-advisors have the potential to revolutionise the financial service industry by making it more accessible and affordable. This study provides a comprehensive overview of robo-advisors in the arena of financial markets and investments and their gaining popularity in the fintech industry, particularly in emerging markets like India. It also discusses the changing landscape of the financial sector in India, benefits and challenges of fintech, and the legal and ethical implications of roboadvisors. The current study presents a comparative study between India and UK markets in terms of acceptance and penetration of robo-advisors. It highlights leading robo-advisory firms in India. The data visualisation is done with the help of Microsoft Power BI and Microsoft Excel on the statista survey data. The expected results of this study assist several stakeholders, such as academicians, researchers, investors, stock brokers, regulators, and policy makers. 2024, IGI Global. All rights reserved. -
Nature of music engagement and its relation to resilient coping, optimism and fear of COVID-19
The COVID-19 pandemic has resulted in unprecedented lockdowns, a work from home culture, social distancing and other measures which badly affected the world populace.Individuals over the globe reported experiencing several psychosocial and psychosomatic problems.Nevertheless, this pandemic allowed us to be with ourselves, to understand the importance of healthy lifestyles and to devote time to our passions and hobbies when we were socially isolated.Against this background, the present study was undertaken to explore the nature of peoples everyday musical engagement and to examine how the experience and functions of music were related to resilient coping, life orientation and fear from COVID-19.In an online survey, a total of 197 participants responded to a questionnaire designed to assess the nature of musical engagement (level of musical training, functional niche of music, listening habits and involvement in musical activities), functions of music (FMS), resilient coping (BRCS), life orientation (LOT-R), and fear of COVID-19 (FCV-19S).Results indicate that for most of the respondents, music listening was a preferred activity during the pandemic which resulted in positive effects on their mood, heart rate and respiratory rates.More than 80 per cent of respondents reported music as a source of pleasure and enjoyment and claimed that it helped to calm them, release their stress, and help them relax.Significant positive correlations were found between the functions of music (memory-based and mood-based), optimism and resilient coping and mood-based functions of music and optimism were found to predict resilient coping among individuals.These results suggest that meaningful and active music engagement may lead to optimism which may result in effective resilient coping during the crisis.Moreover, reflecting upon our everyday musical engagements can promote music as a coping skill. 2025 selection and editorial matter, Asma Parveen and Rajesh Verma; individual chapters, the contributors. -
Leveraging unsupervised machine learning to optimize customer segmentation and product recommendations for increased retail profits
The retail sector's success hinges on understanding and responding adeptly to diverse consumer behaviours and preferences. In this context, the burgeoning volume of transactional data has underscored the need for advanced analytical methodologies to extract actionable insights. This research delves into the realm of unsupervised machine learning techniques within retail analytics, specifically focusing on customer segmentation and the subsequent recommendation strategy based on clustered preferences. The purpose of this study is to determine which unsupervised machine learning clustering algorithms perform best for segmenting retail customer data to improve marketing strategies. Through a comprehensive comparative analysis, this study explores the performance of multiple algorithms, aiming to identify the most suitable technique for retail customer segmentation. Through this segmentation, the study aims not only to discern and profile varied customer groups but also to derive actionable recommendations tailored to each cluster's preferences and purchasing patterns. 2024, IGI Global. All rights reserved. -
Revolutionizing healthcare telemedicine's global technological integration
The pursuit of universal and high-quality healthcare services is a fundamental obligation of any responsible state, yet India faces persistent challenges in achieving this goal despite governmental efforts and policies. Notably, the 65th World Health Assembly emphasized universal health coverage (UHC) as pivotal for global public health advancement. Addressing this, a 2010 high-level expert group identified impediments in UHC implementation, highlighting issues such as health financing, infrastructure, skilled human resources, and access to medicines. This study focuses on exploring telemedicine's potential to mitigate these challenges and become instrumental in realizing universal health coverage in India. It aims to scrutinize government plans, critically assess policies on telemedicine implementation, and propose effective integration models, particularly in rural areas, to facilitate UHC. Additionally, the research aims to examine the role of AI, ML, deep learning, and neutral networks within telemedicine, envisaging their contribution to augmenting telemedicine's efficacy towards achieving universal health coverage in India. 2024, IGI Global. All rights reserved. -
Recommendation of food items for thyroid patients using content-based knn method
Food recommendation system has become a recent topic of research due to increase use of web services. A balanced food intake is significant to maintain individuals physical health. Due to unhealthy eating patterns, it results in various diseases like diabetes, thyroid disorder, and even cancer. The choice of food items with proper nutritional values depends on individuals health conditions and food preferences. Therefore, personalized food recommendations are provided based on personal requirements. People can easily access a huge amount of food details from online sources like healthcare forums, dietitian blogs, and social media websites. Personal food preferences, health conditions, and reviews or ratings of food items are required to recommend diet for thyroid patients. We propose a unified food recommendation framework to identify food items by incorporating various content-based features. The framework uses the domain knowledge to build the private model to analyze unique food characteristics. The proposed recommender model generates diet recommendation list for thyroid patients using food items rating patterns and similarity scores. The experimental setup validated the proposed food recommender system with various evaluation criteria, and the proposed framework provides better results than conventional food recommender systems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Recommendation of diet using hybrid collaborative filtering learning methods
These days, various recommender systems exist for online advertisement services which recommend the products considering users interests. Similarly, health recommendation systems are becoming most important component in individuals life. Due to the modernization and busy schedule, people give less concern to their eating patterns. This leads to various health issues like obesity, thyroid disorder, diabetes and others. Every individual has different health issues and food habits. Therefore, diet recommendations should be suggested by considering their personal health profile and food preferences. So, it becomes essential to analyze individuals health concerns before recommending the diet with required nutrient values. Thus, it helps people to minimize the further risks associated with the current health conditions. The proposed diet and exercise recommender framework suggests a balanced diet for thyroid patients. It takes care of the food intake with necessary nutrients requirement based on thyroid disorders. This paper applies K-nearest neighbor collaborative filtering models using various similarity measures. The paper assessed two-hybrid learning methods, KNN with alternating least squares: KNN-ALS and KNN with stochastic gradient decent: KNN-SGD. The experimental setup analyzed and evaluated the performances of all algorithms using mean absolute error (MAE) and root mean squared error (RMSE) values. Springer Nature Singapore Pte Ltd 2020. -
Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic Review
Nowadays, diet and exercise recommender frameworks have gaining expanding consideration because of their importance for living healthy lifestyle. Due of the expanded utilization of the web, people obtain the applicable wellbeing data with respect to their medicinal problem and available medications. Since diseases have a strong relationship with food and exercise, it is especially essential for the patients to focus on adopting good food habits and normal exercise routine. Most existing systems on the diet concentrate on proposals that recommend legitimate food items by considering their food choices or medical issues. These frameworks provide functionalities to monitor nutritional requirement and additionally suggest the clients to change their eating conduct in an interactive way. We present a review of diet and physical activity recommendation frameworks for people suffering from specific diseases in this paper. We demonstrate the advancement made towards recommendation frameworks helping clients to find customized, complex medical facilities or make them available some preventive services measures. We recognize few challenges for diet and exercise recommendation frameworks which are required to be addressed in sensitive areas like health care. 2019, Springer Nature Singapore Pte Ltd. -
Revolutionizing legal services with blockchain and artificial intelligence
[No abstract available]