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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. -
Human Consumption of Digital Synthetic Outputs: Cross Checking Real or Fake Information
AI-Generated content, deepfakes and synthetic media, people are increasingly wondering how they can discern the difference between genuine and fake. People may become confused, change their minds, and make unwise decisions that put their safety at danger when they see false information and manipulated pictures and videos, such AI-generated deepfakes. AI detection techniques, and teaching people how to read and write have all helped with these difficulties. Blockchain technology analyzes the digital history of anything to make sure it is authentic. Two significant steps toward making things more open are regulatory frameworks and ethical AI practices. Users should be vigilant and examine a lot of trustworthy sources, including reverse image search or deepfake detectors. The influence of incorrect information will be lessened if individuals are more informed and encouraged to utilize internet resources properly. To use another metaphor, let's make sure that people know what they're talking about when we speak about synthetic things. 2026, IGI Global Scientific Publishing. All rights reserved. -
Human factors and social engineering in IoT attacks
This chapter explores how social engineering and human factors contribute to IoT assaults, emphasizing how human mistake and psychological manipulation weaken linked systems. Cybercriminals are increasingly using social engineering techniques to trick people into disclosing private information or jeopardizing security measures as IoT devices become more and more integrated into everyday life and vital infrastructure. This study examines important strategies like baiting, pretexting, and phishing, highlighting their unique uses in Internet of Things settings. It also looks into how human behavior affects security procedures, showing how dangers are increased by insufficient awareness and training. The chapter offers important insights into how human factors and IoT security interact by examining current case studies and actual attack scenarios. It ends with tactical suggestions for enhancing security awareness, fortifying authentication procedures, and putting in place efficient defenses against social engineering risks in IoT ecosystems. Future research should focus on developing AI-driven security measures, flexible defense strategies, and strong policy frameworks to strengthen IoT security. Tackling these human-related vulnerabilities is essential to building a safer and more reliable IoT ecosystem. 2026 Elsevier Inc. All rights reserved.. -
Human gut microbiota and regulation of human behavior
The human body is often referred to as a microbiome, embodying a multitude of microbes. Specifically, the gut microbiome constitutes a cluster of bacteria within the gastrointestinal (GI) system. In both healthy individuals and those facing health issues, these bacteria significantly impact human physiology. They form an integral part of the unconscious system that exerts influence over human behavior. Globally, researchers are actively investigating the components of a good gut microbiome to enhance our comprehension of the role gut microorganisms play in health and disease. The gut harbors a diverse array of bacteria, encompassing both hazardous and beneficial types. Furthermore, studies on specific health-improving parasitic species are yielding biological insights that may propel the development of novel drugs. The gut microbiome directly impacts an individual's health by secreting physiologically active compounds like vitamins, essential amino acids, and lipids. Moreover, it indirectly influences the immune system and metabolic functions, thereby affecting moods and cognitive functions. Recognition of the role played by gut microbiota extends to various diseases and disorders. Evidence demonstrates significant interactions between the microbiome and certain drugs, profoundly shaping their intended effects. The interplay between the immune system, gut microbiota, and psychiatric disorders such as eating disorders, as well as conditions like cancer, autoimmune diseases, and autism spectrum disorder, is increasingly evident through ongoing research. This is unsurprising given the pivotal roles played by daily caloric intake, eating habits, and food composition in regulating various biological systems. Recent research sheds light on the intricate gut-brain axis (GBA), a two-way communication system where the gut receives signals from the brain, and vice versa. Microbes in the GI tract produce neurotransmitters that influence crucial functions like learning, memory, attention, and emotions. Even a slight change in the gut microbiome can lead to inflammatory reactions, connecting to the hypothalamic-pituitary-adrenal (HPA) axis and influencing human behavior. Under stressed conditions, changes in the gut microbiome can harm helpful bacteria, reduce the variety of bacteria in our gut, and allow harmful bacteria to grow. This makes us more likely to get sick and causes inflammation in our gut. Some studies even suggest that chemicals produced by the gut during an infection can affect our brain, making us more prone to feeling anxious or depressed. Knowing that our gut microbiome influences our behavior, it's essential to pay attention to what we eat, avoid drugs that harm our gut bacteria, and consider taking probiotic supplements in our diet. 2025 Elsevier Inc. All rights reserved. -
Human heart disease prediction system using data mining techniques
Nowadays, health disease are increasing day by day due to life style, hereditary. Especially, heart disease has become more common these days, i.e. life of people is at risk. Each individual has different values for Blood pressure, cholesterol and pulse rate. But according to medically proven results the normal values of Blood pressure is 120/90, cholesterol is and pulse rate is 72. This paper gives the survey about different classification techniques used for predicting the risk level of each person based on age, gender, Blood pressure, cholesterol, pulse rate. The patient risk level is classified using datamining classification techniques such as Nae Bayes, KNN, Decision Tree Algorithm, Neural Network. etc., Accuracy of the risk level is high when using more number of attributes. 2016 IEEE. -
Human Resource (HR) Analytics and Its Modus OperandiAn Explorative Study
People management immensely benefited from predictive HR analytics that provide deeper insight into employee data for effective decision-making. HR analytics has contributed to formulating and implementing data-driven strategies across various industries and organizations. The main objective of this conceptual paper is to explain the concept of HR analytics and describe its modus operandi. We reviewed the literature on various facets of HR analytics from 2016 to 2024. We have explained the modus operandi of HR analytics through the key aspects of strategic HRM. This paper is significant because HR analytics is evolving as a more technical discipline with modern technologies such as machine learning and artificial intelligence. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Human Resource Development and Economic Growth: Leveraging India's Youthful Population
This empirical investigation employs a multivariate time series framework utilizing the Vector Error Correction Model (VECM) to unravel the intricate long- run equilibrium and short- run dynamics among GDP, youth population, and youth literacy in India for the time- period 1990 to 2024, revealing through Johansen cointegration test the existence of two statistically significant cointegrating vectors, with normalized equations underscoring the pivotal elasticity- driven relationships between economic growth and demographic- literacy indicators, while adjustment coefficients demonstrate the system's endogenous correction mechanism toward long- run equilibrium, and the impulse response and variance decomposition analyses substantiate the dominance of literacy shocks in driving GDP variability over extended horizons, thereby yielding profound macroeconomic implications for policy formulation directed at enhancing human capital and ensuring sustainable. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Human Resource Development Climate and its Impact on Stress Among Teachers in Unaided Education Institutions in Bangalore
Global Journal of Finance & Management, Vol-4 (4), ISSN-0975-6477 -
Human Resource Management in Digital India
The business entities of today are aware of the vital role played by technological interventions in value creation. HR technological interventions are no exceptions either. These interventions are aligned to business goals and help businesses achieve their bottom lines. Nevertheless, some business owners are apprehensive about the way forward while adopting technology. This chapter focuses on the various technologies that aid HR functions, its implementation framework keeping in perspective the key apprehensions, considerations and competency requirements. The chapter highlights few Indian organizations that have adopted it. The findings show that emphasis is laid on business value creation for stakeholders due to HR technology adoption. 2023 by World Scientific Publishing Co. Pte. Ltd. All right reserved. -
Human Resource Management in the Power Industry Using Fuzzy Data Mining Algorithm
Currently, database and information technology's frontier study area is data mining. It is acknowledged as one of the essential technologies with the greatest potential. Numerous technologies with a comparatively high level of technical substance are used in data mining, including artificial intelligence, neural networks, fuzzy theory, and mathematical statistics. The realization is challenging as well. Job satisfaction is one of several factors that cause employees to leave or switch jobs, and it is also closely tied to the organization's human resource management (HRM) procedures. It is continuously difficult and at times beyond the HR office's control to keep their profoundly qualified and talented specialists, yet data mining can assume a part in recognizing those labourers who are probably going to leave an association, permitting the HR division to plan a mediation methodology or search for options. We have analysed the major thoughts, techniques, and calculations of affiliation rule mining innovation in this article. They effectively finished affiliation broadcasting, acknowledged perception, and eventually revealed valuable data when they were coordinated into the human resource management arrangement of schools and colleges. 2023 IEEE. -
Human rights and religion : Perspectives and retrospectives /
Asian Journal Of Research In Social Science & Humanities, Vol.6, Issue 1, pp.80-89, ISSN: 2249-7315. -
Human Voices and Algorithmic Echoes: Resignifying Transfeminine Experiences Through Hybrid Poetics Framework
Advances in generative artificial intelligence (AI) are reshaping cultural production, yet questions remain about whether machine-authored texts can authentically represent marginalized lives. This study examines the capacity of OpenAIs GPT-4o to represent the lived experiences of transfeminine individuals using a three-phase Hybrid Poetics Framework (HPF). Ten transfeminine participants were recruited through purposive maximum variation sampling. Semi-structured interviews and a participatory focus group (FGD-1) informed the creation of both human-generated poems (HGPs) and AI-generated poems (GPT-4o poems). In the reception phase, participants completed an authorship discrimination task and poetic quality ratings, followed by a structured poetry-reflection focus group (FGD-2). Results show that participants identified authorship above chance and rated HGPs higher for Emotional Quality, Atmosphere, and Structural Quality, while AI poems were praised for polish but perceived as emotionally distant. By contrast, creativity showed overlap across conditions.Findings foreground authenticity, affective fidelity, and ethical risks in AI-mediated representation. We propose bounded legibility and community co-governance as safeguards and introduce the Hybrid Poetics Framework (HPF) as a methodological approach for operationalizing participatory evaluation in Human Computer Interaction (HCI). 2026 The Author(s). Published with license by Taylor & Francis Group, LLC.