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How tourist motivations shape perceptions of service quality at pilgrimage sites
Previous research has not adequately examined how various tourist motivations affect perceived service quality at pilgrimage destinations. This study seeks to investigate the effect of various motives religious pilgrimage, votive offerings, leisure, and meditation on service quality perceptions at seven Jyotirlinga pilgrimage destinations in North India. A cross-sectional survey of 1047 visitors was carried out, and data were analysed through one-way ANOVA to determine significant differences between visitor groups. For multiple comparisons, Bonferroni and Games-Howell post hoc tests were used depending on the homogeneity of variances. The results show differences in service quality perceptions, specifically in desired facilities, safety and security, and transportation. Pilgrims interested in religious devotion emphasised safety, whereas leisure travellers gave more importance to the quality of facilities available and transportation. These findings have practical implications for pilgrimage site management, highlighting the importance of making targeted improvements in service delivery to meet the expectations of various visitor segments. Copyright 2025 Inderscience Enterprises Ltd. -
How well the log periodic power law works in an emerging stock market?
A growing body of research work on Log Periodic Power Law (LPPL) tries to predict market bubbles and crashes. Mostly, the fitment parameters remain con?ned within certain specific ranges. This paper examines these claims and the robustness of the reformulated LPPL model of Filimonov & Sornette (2013) for capturing large falls in the S&P BSE Sensex, an Indian heavyweight index over the period 20002019. Thirty-five mid to large-sized crashes are identified during this period, forming a clear LPPL signature. This confirms the possibility to predict the embedded risk of future uncertain events in the Indian stock market with the LPPL approach. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
HQA Bot: Hybrid AI Recommender Based Question Answering Chatbot
The COVID pandemic has presented a number of challenges for education, particularly when it comes to reaching and engaging students. As a result, online education has become increasingly important, and artificial intelligence (AI) has played a crucial role in supporting this shift. The proposed tutor assistance question-answering system uses AI to automatically generate responses to student questions. This system includes a feedback mechanism, known as a satisfaction index that measures the efficiency of the generated responses and suggest relevant follow-up questions. The proposed Hybrid Recommender-based Dijkstras algorithm (HRD) improves the system's accuracy. This algorithm uses a combination of techniques to group relevant questions based on context, which improves the accuracy of identifying the next relevant question. In our customized dataset, this approach achieved an accuracy of 96% and an average accuracy of 82% across benchmarked datasets. With this system, we aim to bridge the gap between students and education by providing a more engaging and personalized learning experience. 2023, Ismail Saritas. All rights reserved. -
HR 4.0: Integrating AI and automation in human resource development
Artificial Intelligence (AI) and automation within Human resource development (HRD) under the concept of HR 4.0 are revolutionizing HR practices by enhancing efficiency, accuracy, and strategic decision-making. These technologies transform key HRD processes such as training, talent management, and employee engagement, driving organizational success. This study systematically reviews the integration of AI and automation practices within Human resource development under the concept of HR 4.0. It examines how these technologies are transforming HRD practices, including training, talent management, performance evaluation, and employee engagement. Through a comprehensive analysis of existing literature from library data from published electronic and printed databases, the study identifies key trends, benefits, and challenges associated with artificial intelligence and automation techniques in Human Resource Development practices. The narrative synthesis of findings reveals the profound impact of these technologies on improving efficiency, accuracy, and strategic decision-making in HRD processes. Furthermore, the review highlights ethical considerations, such as data privacy and algorithmic bias, and offers practical insights for HRD practitioners aiming to leverage AI and automation effectively. It also recognizes research gaps and suggests future research directions to further explore the potential of Artificial Intelligence and automation in enhancing human resource development. 2026 Pushan Kumar Dutta, Amarnath Padhi, Sulagna Das, Vinod Kr Sharma and Poshan Yu. All rights reserved. -
HR Analytics in an Era of Rapid Automation
Human Resources (HR) departments often have significant data sets related to employees and positions within their organizations, but optimizing use of this data can present challenges. As the business world rapidly transforms due to technological advancements, experts within the HR domain must learn to effectively use data to improve workforce performance and assist with strategic decisions. A comprehensive understanding of HR analytics and its multiple levels, ranging from descriptive to perspective, can emphasize how the data can support, track, and monitor employee performance, culture, turnover rate, and absenteeism. HR Analytics in an Era of Rapid Automation is a valuable guide for academics, researchers, and practitioners interested in the latest developments in HR analytics. It covers relevant theories and conceptual models based on quantitative and qualitative findings and emphasizes the importance of utilizing HR analytics for sustainable decision making. With a focus on recruitment analytics, talent acquisition, employee performance analytics, and more, this book provides practical solutions to the challenges facing HR professionals in the rapidly changing business world. By highlighting the value of people and HR analytics for business success, this book offers several solutions for the analysis of challenges facing HR professionals today. 2023 by IGI Global. All rights reserved. -
HR analytics in employee engagement and turnover
HR Analytics has expanded increasingly in the last two decades. Nowadays, several Companies use the supremacy of analytics, in gaining a competitive edge over others by recognizing all essentials of the employees. Organizations aim to optimize employee-performance, and hence are making use of HR analytics to drive strategic HR decisions. This study examines the advent of HR analytics by in measuring and improving employee engagement and turnover. Employee engagement analytics stands for exercising the use of data in decision-making process by integrating employee engagement with other HR and non-HR data. Employee engagement analytics is a subsection of workforce-analytics. It has become the standard contemporary system in advanced employee administration and retention. Employee engagement analytics benefits all stakeholders within the organization. 2023, IGI Global. All rights reserved. -
HR Analytics: An Indispensable Tool for Effective Talent Management
Business organizations have changed tremendously in the way they visualize the human capital of the organization and make all efforts to create a workforce that is productively engaged and is ready to embrace the challenges posed by uncertainty and turbulence in the business environment. This calls for a decision-making approach that is based on observed people behaviours rather than relying on intuition and gut feel. These observed behaviours are reactions or consequences to stimuli and therefore the science of Human Resource Management can be better understood as predicting these dependent variables based on a set of independent variables. This chapter attempts to present a complete framework of HR analytics in terms of concept, need and how it can add immense value to effective talent management in the organizations. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
HRL-ViT: Human-Robot Collaborative Vision Transformer for AIoT-Enabled Leaf Disease Detection in Precision Agriculture
The combination of artificial intelligence and Internet of Things (AIoT) technologies is changing precision agriculture by making it possible to automatically check the health of crops. Early detection of leaf diseases is still important for stopping yield losses, but regular convolutional neural networks (CNNs) often don't work as well when they have to deal with different textures, lighting changes, and noise on the field level. To address these constraints, this study presents HRL-ViT, a Human-Robot Collaborative Learning framework that utilizes Vision Transformers for leaf disease identification. The frame-work merges the global attention feature of Vision Transformers with a human-in-the-loop approach, wherein predictions with low confidence are validated by experts and used to improve the model over time. The system is also made for edge-based AIoT deployment, which lets you analyze data in real time in agricultural settings. Experimental research utilizing both benchmark datasets and field-acquired images demonstrates that HRL-ViT consistently surpasses baseline CNN and Transformer models, attaining superior accuracy, precision, and recall while minimizing false detections. Transformers' attention maps can be visualized to make them even easier to understand, which helps users trust them and make decisions. In general, HRL-ViT shows a lot of promise for use in autonomous robotic platforms. It offers an explainable and scalable way to find diseases in precision agriculture. 2025 IEEE. -
HRM challenges and employee wellbeing in the tourism industry: Moderating role of organizational culture
The current study aims to understand the Challenges faced by Tourism sector employees and its effect on employee well being. Also the moderating effect of Organizational culture is measured. Staff members of airlines, hotels, and travel agencies provided the main data used in this quantitative analysis. A total of 122 individuals were deemed suitable for the study according to the Kregcie-Morgan table. Using the HRM Challenges, Employee Wellbeing, and Organizational Culture measure as a starting point, a carefully crafted questionnaire was developed. Gaskins' master validity table was used to evaluate the reliability and validity of the questionnaire. Analysis of the data was done using SPSS for factor analysis and confirmatory factor analysis with. The findings show that there is a negative impact of HRM challenges on the employee wellbeing. But when organization culture is introduced as a moderator, it dampens the effect of challenges on employee wellbeing. Therefore, to reduce the impact of challenges on employee wellbeing, Organizational culture is the key. 2025, IGI Global Scientific Publishing. -
HTLML: Hybrid AI Based Model for Detection of Alzheimers Disease
Alzheimers disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brains ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Nae base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective. 2022 by the authors. -
Hubble Space Telescope Captures UGC 12591: Bulge/disc properties, star formation and 'missing baryons' census in a very massive and fast-spinning hybrid galaxy
We present Hubble Space Telescope ( HST ) observations of the nearby, massive, highly rotating hybrid galaxy UGC 12591, along with observations in the UV to FIR bands. HST data in V , I , and H bands is used to disentangle the structural components. Surface photometry shows a dominance of the bulge o v er the disc with an H-band B / D ratio of 69 per cent . The spectral energy distribution (SED) fitting reveals an extremely low global star formation rate (SFR) of 0 . 1-0 . 2 M yr-1 , exceptionally low for the galaxy's huge stellar mass of 1 . 6 0 11 M, implying a strong quenching of its SFR with a star formation efficiency of 3-5 per cent. For at least the past 10 8 yr, the galaxy has remained in a quiescent state as a sterile, 'red and dead' galaxy. UGC 12591 hosts a supermassive black hole (SMBH) of 6 . 18 0 8 M, which is possibly quiescent at present, i.e. we neither see large (1kpc) radio jets nor the SMBH contributing significantly to the mid-IR SED, ruling out strong radiative feedback of AGN. We obtained a detailed census of all observable baryons with a total mass of 6 . 46 0 11 M within the virial radius, amounting to a baryonic deficiency of 85 per cent relative to the cosmological mean. Only a small fraction of these baryons reside in a warm/hot circumgalactic X-ray halo, while the majority are still unobservable. We discussed various astrophysical scenarios to explain its unusual properties. Our work is a major step forward in understanding the assembly history of such e xtremely massiv e, isolated galaxies. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
HULA: Dynamic and Scalable Load Balancing Mechanism for Data Plane of SDN
Multi-rooted topologies are used in large-scale networks to provide greater bisectional bandwidth. These topologies efficiently use a higher degree of multipathing, probing, and link utilization. An end-to-end load balancing strategy is required to use the bisection bandwidth effectively. HULA (Hop-by-hop Utilization-aware Load balancing Architecture) monitors congestion to determine the best path to the destination but, needs to be evaluated in terms of scalability. The authors of this paper through artifact research methodologies, stretch the scalability up to 1000 nodes and further evaluate the performance of HULA on software defined network platform over ONOS controller. A detailed investigation on HULA algorithm is analysed and compared with four proficient large-scale load balancing mechanisms including: connection hash, weighted round-robin, Data Plane Devlopment Kit (DPDK) technique, and a Stateless Application-Aware Load-Balancer (SHELL). 2023 IEEE. -
Human Activity Analysis Based on Smartphones and Smart Glasses
The study explores the application of smart glasses and smartphones to study human behavior. Through ensemble and deep learning methodologies, the study seeks to autonomously scrutinize data from each device to improve accuracy and resilience in activity identification. The methodology adopted entails the utilization of distinct models for data derived from smartphones and smart glasses, as opposed to amalgamating attributes, to acquire distinctive insights into user activities. The study outcomes demonstrated promising results, showcasing elevated precision in activity recognition across various machine learning models. Comparative analyses with prior research work reveal enhancements in algorithmic efficacy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Human activity recognition using wearable sensors
The advancement of the internet coined a new era for inventions. Internet of Things (IoT) is one such example. IoT is being applied in all sectors such as healthcare, automobile, retail industry etc. Out of these, Human Activity Recognition (HAR) has taken much attention in IoT applications. The prediction of human activity efficiently adds multiple advantages in many fields. This research paper proposes a HAR system using the wearable sensor. The performance of this system is analyzed using four publicly available datasets that are collected in a real-time environment. Five machine learning algorithms namely Decision tree (DT), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (kNN), and Support Vector Machine (SVM) are compared in terms of recognition of human activities. Out of this SVM responded well on all four datasets with the accuracy of 77%, 99%, 98%, and 99% respectively. With the support of four datasets, the obtained results proved that the performance of the proposed method is better for human activity recognition. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Human AI: Explainable and responsible models in computer vision
Artificial intelligence (AI) is being used in all areas of information, research, and technology. Allied parts of AI have to be investigated for understanding the association among them. Human and explainable AI (XAI) are a few examples that can help in the development of understandable systems. Posthoc actions and operations are geared toward explainable AI, which investigates what went wrong in a black box setting. Responsible AI, on the other hand, seeks to avoid such blunders in the first ring. Ontology is defined as the study of existence and has several applications in computer science, specifically in platforms such as Resource Description Framework and Web Ontology Language. In this chapter, we examine both parts of the aforementioned AI and attempt to establish a link between ontology and explainable AI as they complement each other in terms of creating trustworthy systems. As part of the chapter, an applicable literature is also brought in, emphasizing the necessity of current understanding in explainable and responsible AI. For illustrating the lineage of input and output operations in relation to ontology characteristics and AI, a scenario of AI implementation using image processing dataset is studied. Classroom learning is an integral element of every student's daily life. Assessing the interest levels of individual pupils would help in enhancing the process of teaching and learning. This work contributes to the process of explainable AI by eliciting algorithms that can extract faces from frames, recognize emotions, conduct studies on engagement levels, and provide a session-wide analysis. Detailed descriptions of these operations, as well as specific parameters, are provided to relate the theme of work. We feel that this collaboration between ontology and explainable AI is unique in that it acts as a springboard for future study in these domains. 2024 Elsevier Inc. All rights reserved. -
Human behavior analysis of BBC-news comments posted on facebook using lexicon-rule based approach
Today people spend a considerable part of their time on online platforms say, social media than with the real world. Social media, particularly Facebook is the platform for the users to post, share, like, tag and comment any photos and videos. This paper deals with the Facebook platform to study the human behavior based on the comments of five posts from BBC-news Facebook page. For every post in Facebook we can get different opinion or emotional behavior by different users. The behavior of people to the same event need not be similar, they can be different. A response through comments and smileys for a post portrays behaviors of people. Here the behavior analysis is performed on comments of the BBC news Facebook posts. The comments of the post are fetched by the online extractor named Socialfy [12]. This paper considered five news from unique from BBC-news Facebook page. The human behavior analysis performed using Python VADER (Valence Aware Dictionary and Sentiment Reasoner) package. This work uses the Lexicon approach to assign scores for the words and rule-based approach used to find the polarity type of words. The polarity of a post is the sentimental behavior of the people towards the post. The total polarity of this work tends towards neutral so, we could conclude that for each situation behavior of man can take positive or negative poles. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Human behavior analysis on political retweets using machine learning algorithms
The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popular way for people to share what they are thinking. Many people express their thoughts on various issues relating to their hobbies. As a result, microblogging websites have become a valuable resource for opinion mining and sentiment research. Twitter is a well-known microblogging network, with over 500 million new tweets posted daily. The goal of this study was to mine tweets for political sentiments. The extraction of tweets relating to India's well-known political leaders of different states & parties in India and applying the polarity detection analysis of human behavior on the retweeted messages As a result, the sentiment classification algorithm is designed to determine whether tweets are more likely to predict the popularity of certain politicians among the general public. The subjectivity and polarity present in the tweets of political leaders are compared. The engagements of these leaders are then taken into account to determine their popularity. All these comparisons are then portrayed using data visualizations. 2023 The Authors -
Human Body Pose Estimation and Applications
Human Pose Estimation is one of the challenging yet broadly researched areas. Pose estimation is required in applications that include human activity detection, fall detection, motion capture in AR/VR, etc. Nevertheless, images and videos are required for every application that captures images using a standard RGB camera, without any external devices. This paper presents a real-time approach for sign language detection and recognition in videos using the Holistic pose estimation method of MediaPipe. This Holistic framework detects the movements of multiple modalities-facial expression, hand gesture and body pose, which is the best for the sign language recognition model. The experiment conducted includes five different signers, signing ten distinct words in a natural background. Two signs, 'blank' and 'sad, ' were best recognized by the model. 2021 IEEE. -
Human capital challenges in sustainability start-ups
Sustainability start-ups face unique human capital challenges (HCCs) like lack of brand awareness, competition, turnover, burnout, limited growth opportunities, resources, and expertise gaps. This mixed-methods research chapter examines strategies to address these challenges, surveying 200 start-ups and interviewing 20 CEOs and HR managers. Findings advocate for investment in brand awareness, unique benefits, positive work environments, professional development, and transparent career paths. Effectiveness varies based on start-up needs, but HCCs significantly impact performance. Prioritizing talent attraction, retention, and development is crucial for sustainability start-ups. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Human cognition and emotional response towards visual environmental features in an urban built context: a systematic review on perception-based studies
Urban built environments can influence human cognitive and emotional comforts. Human comfort in the built environment has challenged architects and urban designers while developing comfortable spaces. Emerging cognitive-architectural studies in architecture engineering inform new directions for improvising human spatial design practices. This paper intends to present a systematic meta-analysis of selected empirical studies to identify the gaps and future scope of research in human cognition and built environments. However, the scope of the literature review is to concentrate on experiments that consider physiological reading in different environments, such as nature and architectural spaces in cognitive study areas. The peer-reviewed literature published from 2010 to 2021 illustrates that only limited design parameters are considered in these experiments. The study analyses the extensive consideration of experimental medium, simulation categories, and participant factors like gender and age in this research domain. The survey recommends considering more visual features, contextual conditions, and ethnic groups. 2023 Informa UK Limited, trading as Taylor & Francis Group.