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Neuroleadership strategies: Elevating motivation and engagement among employees
In the ever-evolving landscape of the modern era, organizations face the ongoing challenge of maintaining motivated and engaged employees. Despite the substantial body of research on this topic, many organizations still struggle to effectively promote engagement and motivation among their employees. This research aims to investigate the application of neuroleadership strategies in addressing this issue. The SCARF model, based on neuroscience principles, provides a valuable framework for understanding neuroleadership strategies which address social and emotional triggers that impact engagement and motivation. It can be effectively used to drive motivation and engagement in the workplace by addressing the fundamental social and emotional needs of employees. This study employs a quantitative approach which assesses the 321 employees from different organizations in India. The results of the study would provide leaders with practical insights to boost motivation and engagement in organizations and thereby improve the effectiveness of the organization. 2024, IGI Global. All rights reserved. -
Neuropsychological functions and optimism levels in stroke patients: A cross-sectional study
Neuropsychological abnormalities, as well as behavioural and psychological characteristics, are being examined in patients in order to determine the prevalence of cognitive impairment and other neurovascular riskfactors, including prior strokes. The green light has been given by the institution's human ethics committee for this investigation. In order to conduct the study, the researchers used experimental clinical research techniques. Seventy-five stroke patients ranging in age from 20-70 were the focus of this study. All patients in the hospital had daily clinical examinations and were able to identify the underlying causes of their strokes. The NIMHANS Neuropsychological Battery was administered to all patients between one and six months after the onset of their stroke symptoms. 2023, IGI Global. All rights reserved. -
Neuroscience of social understanding
Comprehending human behavior and interactions requires an understanding of the social mind. Social cognitive neuroscience provides a lens to understand these complexities. This chapter explores the core brain mechanisms that control social conduct by exploring the field of social cognitive neuroscience. It examines aspects of social cognition, like the theory of mind, social perception, empathy, and decisionmaking. It explains how the brain helps navigate complex social contexts by looking at complex interactions between neurological processes and social behaviors. Important subjects include the function of the mirror neuron system, temporoparietal junction and prefrontal cortex in mediating social cognition. It discusses the implications of social cognitive neuroscience for understanding diseases such as schizophrenia and autism spectrum disorder, which are characterized by social deficiencies. Through this research, we learn about the social mind and its brain foundations, and it opens the door to novel interventions that improve interpersonal relationships and social well-being. 2024 by IGI Global. All rights reserved. -
New bounds of induced acyclic graphoidal decomposition number of a graph
An induced acyclic graphoidal decomposition (IAGD) of a graph G is a collection ? of nontrivial induced paths in G such that every edge of G lies in exactly one path of ? and no two paths in ? have a common internal vertex. The minimum cardinality of an IAGD of G is called the induced acyclic graphoidal decomposition number denoted by ? ia (G). In this paper we present bounds for ? ia (G) in terms of cut vertices and simplicial vertices of G. Springer Nature Switzerland AG 2019. -
Nifty index: Integrating deep learning models for future predictions and investments
The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors. -
NONHUMAN VISIONS: From Experimental Cinema to Hollywood
In this chapter, I want to trace the convergences between experimental cinema, video-art practices and Hollywood that has emerged as a result of their mutual investment in capturing the visuality of the Anthropocene through a technologically produced and mediated sensory framework. Through a series of case studies from independent filmmakers and Hollywood blockbusters, I argue that as much as the avant-garde is invested in producing Anthropocenic imaginations, Hollywood has also been pursuing it by creating a series of affective strategies that help us to conceive and relate to an otherwise incomprehensible scales of deep-pasts and futures. 2024 selection and editorial matter, Simi Malhotra, Sakshi Dogra and Jubi C. John; individual chapters, the contributors. -
Novel magnetic nanocomposites and their environmental applications
Environmental contamination by numerous emerging pollutants including pharmaceuticals, microplastics, and pesticides residues is one of the greatest problems facing the world today. The release of these pollutants into the air, water, and soil causes serious threat to plants and animals. These contaminants enter the food chain through contaminated agricultural produce and animals, posing a threat to human health. Therefore, there is an urgent need to develop novel methods to detect, degrade, and remove toxic environmental pollutants. Recently, nanomaterials have been widely used in various applications as catalysts, sensors, and adsorbents due to their unique outstanding properties. This chapter, therefore, focuses on the recent application of magnetic nanoparticles and their respective nanocomposites as degradation catalysts, adsorbents, and electrochemical sensors for detection and removal of environmental pollutants. 2024 Elsevier Ltd. All rights reserved. -
Nurturing employee well-being and mental health: The cornerstone of retention strategies
This chapter explores the critical importance of employee mental health and well-being concerning global talent procurement and retention strategies. This study examines the dynamic nature of mental health in the workplace, emphasizing its potential to improve employee retention. Through an analysis of contemporary methodologies, obstacles, and inventive resolutions, this chapter aims to furnish organizations endeavouring to establish healthier and more efficient work environments with valuable insights. Furthermore, the chapter predicts forthcoming developments and trends in this crucial field. 2024, IGI Global. -
Nurturing the Rudiments and Use Cases of Ongoing Natural Language Generation for a Future Profitable Business More Profitable
Decoding the world of artificial intelligence and its usage in the current intelligence landscape enhance bottom-up growth in building resilient global business. The areas of artificial intelligence (AI) concerned with human-to-machine and machine-to-human interaction. The Next Wave in AI-driven speech is Natural Language Generation (NLG). Natural Language Generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narrative from a dataset. NLG is related to computational linguistics, natural language processing (NLP) and natural language understanding (NLU). NLG research often focuses on building computer programs that provide data points with context. Sophisticated NLG software has the ability to mine large quantities of numerical data, identify patterns and share that information in a way that is easy for humans to understand. The speed of NLG software is especially useful for producing news and other time-sensitive stories on the internet. At its best, NLG output can be published verbatim as web content. The goal of Natural language generation (NLG) is to use AI to produce written or spoken narrative from a dataset. Therefore, this study aims to study how NLG enables machines and humans to communicate seamlessly, simulating human to human conversations and using NLG how organizations are building new customer experiences, monetizing information assets, introducing new offerings and streamlining operational costs. Therefore, the coverage of this chapter will answer to the industrialists and new start-ups. What can NLG do for business? and what are the future applications of NLG? 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Nutraceuticals to prevent and manage cardiovascular diseases
Unhealthy lifestyle and diet are the key risk factors to cardiovascular diseases. A healthy cardiac and vascular system can prevent cardiovascular-related diseases like hypertension, atherosclerosis, and heart stroke. Identifying pharmacologically important metabolites had paved the way to contemporary medicine. People are more attracted to them as they are majorly plant-based metabolites such as polysaccharides, polyphenols, polysterols, and vitamins as cardio-protectors. Preclinical, clinical, and animal studies provide substantial data confirming nutraceuticals as a promising therapeutic agent in curing cardiovascular diseases. This chapter summarizes on major bioactive molecules as nutraceuticals with preclinical and clinical studies, emphasising their cardiovascular protective roles. 2023 Elsevier Inc. All rights reserved. -
Occupancy improvement in serviced apartments: Customer profiling
Sustaining and improving higher occupancy and generating steady revenue by bringing the experience of Home away from Homefor the Customers is the business model of ServicedApartments Industry. Serviced Apartment Industry has to be highly competitive. Its performance is governed by many factors such as competition, technology, social factors and lastly Customers themselves. This study focuses only on Customer profile. To achieve results, the Serviced Apartment Owners/Managers will need to study Customers profile and their needs. Customer satisfaction and retention lead to better customer loyalty, occupancy rates, and revenue. In this paper a methodological framework to analyze and profile Serviced Apartment Customers is discussed, focusing on the factors and particularly the Customer information which could help in increasing the Occupancy. There is a trend that would normally go unnoticed if analysis of data is taken at the aggregate level but looking at them individually, it provides interesting information. 2012 Taylor & Francis Group, London. -
Occupancy Monitoring to Prevent Spread of COVID-19 in Public Places Using AI
The chapter aims to automate the counting of people for occupancy monitoring and send an alert email if the occupancy exceeds the defined threshold in case of restricted occupancy guidelines. The study aims to reduce the manual error, effort, and time for people counting and provide a tool for footfall analysis. We propose and implement an occupancy monitoring system by counting the number of people entering and exiting a building/room using cameras and machine learning (ML) algorithms. The Single Shot Detector (SSD) algorithm, which is based on the MobileNet architecture, is used. This project provides an effective process for execution using either a recorded video file or a live stream from a camera. As the system automates counting people, it reduces human effort and error. It provides accurate results on time. The project can be implemented anywhere using a laptop and a camera for capturing the video. Thus, it provides high portability of the project. The system can leverage pre-installed CCTV cameras and systems in colleges, malls, offices, etc. Thus, it requires less additional expenses and is economically friendly for the organization/decision-making authority. This chapter includes implications for various use cases such as ensuring adherence to COVID-19 guidelines by organizations, streamlining janitorial services, prevention of stampedes, improving indoor air quality, improving electricity efficiency, etc. This project fulfills an identified need to automate the people counting process and generate alerts accordingly. 2025 by Apple Academic Press, Inc. -
Ocr system framework for modi scripts using data augmentation and convolutional neural network
Character recognition is one of the most active research areas in the field of pattern recognition and machine intelligence. It is a technique of recognizing either printed or handwritten text from document images and converting it to a machine-readable form. Even though there is much advancement in the field of character recognition using machine learning techniques, recognition of handwritten MODI script, which is an ancient Indian script, is still in its infancy. It is due to the complex nature of the script that includes similar shapes of character and the absence of demarcation between words. MODI was an official language used to write Marathi. Deep learning-based models are very efficient in character recognition tasks and in this work an ACNN model is proposed using the on-the-fly data augmentation method and convolution neural network. The augmentation of the data will add variability and generalization to the data set. CNN has special convolution and pooling layers which have helped in better feature extraction of the characters. The performance of the proposed method is compared with the most accurate MODI character recognition method reported so far and it is found that the proposed method outperforms the other method. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Offline Character Recognition of Handwritten MODI Script Using Wavelet Transform and Decision Tree Classifier
MODI script is derived from the N?gari family of scripts, and it was used for writing Marathi until twentieth century. Though currently not used as an official script, it has historical importance, as a large volume of manuscripts are preserved at various libraries across India. With the use of an appropriate recognition system, the handwritten documents can be transferred into digital media, so that it can be conveniently viewed, edited, or transliterated to other scripts. The research on MODI script is still in the initial stages, and there is a considerable demand for more research in this field. An implementation of wavelet transform-based feature extraction for MODI scripts character recognition is discussed in this paper. The experiment is performed using Daubechies, Haar, and Symlet wavelets, and performance comparison between these different mother wavelets is carried out. Decision tree classifier is used for the classification process, and the results indicate that the feature extraction using Daubechies wavelet yielded better character recognition result. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Omnichannel supply chain in india: A study using sap-lap approach
Organizations are increasingly adding new service delivery channels to make their products and services readily available in such an environment. Omnichannel retailing will help organizations to provide better consumer service while making it easy for them to do business when appropriately implemented. By implementing omnichannel retailing, organizations will benefit from high operational efficiency, better financial performance, smoother communication, and a satisfied, loyal customer base. Therefore, this study tries to explore the current situation of the retail industry in India, the major actors or players of the business ecosystem, and processes adopted in organizations to provide products and services to their customers. The study will be qualitative in nature and is approached through an SAP (situation-actor-processes)-LAP (learnings-actions-performance) framework to synthesize learning from the current scenario to propose actions and set the performance measures. 2024, IGI Global. All rights reserved. -
On some classes of equitable irregular graphs
Graph labeling techniques are used by data scientists to represent data points and their relationships with each other. The segregation/sorting of similar datasets/points are easily done using labeling of vertices or edges in a graph. An equitable irregular edge labeling is a function $$f: E(G) \rightarrow N$$ (not necessarily be injective) such that the vertex sums of any two adjacent vertices of $$G$$ differ by at most one, where vertex sum of a vertex is the sum of the labels under $$f$$ of the edges incident with that vertex. A graph admitting an equitable irregular edge labeling is called an equitable irregular graph (EIG). In this paper, more classes of equitable irregular graphs are presented. We further generalize the concept of equitable irregular edge labeling to $$k$$-equitable irregular edge labeling by demanding the difference of the vertex sum of adjacent vertices to be $$k \ge 1$$. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Optical Properties of Magnetic Quantum Dots
The delta-like dispersion of the density of states (DoS) enable quantum dots (QD) to display optical and electronic properties comparable with those of real atoms. The discrete electronic structure of QDs akin to that of atoms is formed due to the effect of quantum confinement (QCE). In the case of magnetic quantum dots (MQD), the QDs are incorporated with magnetic impurities such as Mn atom, rare earth elements etc., by which the QDs undergo significant changes in optical and electronic properties by lifting their degeneracies (Zeeman effect). The combination of fluorescent and magnetic entities opens up opportunities for synthesizing two-in-one nanocomposites beneficial for multi-functional, multi-targeting, and multi-theranostic tools. Optical properties of QDs consisting of magnetic impurities, such as the absorption coefficient, oscillator strength, and refractive index are discussed in this chapter. 2023 selection and editorial matter, Amin Reza Rajabzadeh, Seshasai Srinivasan, Poushali Das, and Sayan Ganguly. -
Optimal Charging Strategy for Spatially Distributed Electric Vehicles in Power System by Remote Analyser
The burden on the consumer for the price of fuel for classic vehicles is the root cause for the emergence of the fast growing trend in the power driven vehicles or electric vehicles. Less acceptance of electric vehicles by the customers and the hesitancy to replace traditional fuel powered vehicles by considering the economic factor is a major concern that existing in the current scenario. Therefore, for the proper balancing of the load with respect to the power available among different neighbouring charging stations in a given area, a load scheduling algorithm is used. The optimal route planner for the electric vehicles reaching the charging station is identified and then the power carried by each feeder is calculated by cumulative power of all the charging stations. The identification of the possible route is performed by the spatial network analysis which will be executing at remote analyzer. The location, state of charge, and other details of the electric vehicle through telemetry is used to find the best charging station for the particular vehicle in view of the cost, distance and the time. The performance of the technique is evaluated with and without optimization by considering the logical constraints; and the results are presented. Springer Nature Switzerland AG 2020. -
Optimal DG Planning and Operation for Enhancing Cost Effectiveness of Reactive Power Purchase
The demand for reactive power support from distributed generation (DG) sources has become increasingly necessary due to the growing penetration of DG in the distribution network. Photovoltaic (PV) systems, fuel cells, micro-turbines, and other inverter-based devices can generate reactive power. While maximizing profits by selling as much electricity as possible to the distribution companies (DisCos) is the main motive for the DG owners, technical parameters like voltage stability, voltage profile and distribution losses are of primary concern to the (DisCos). Local voltage regulation can reduce system losses, improve voltage stability and thereby improve efficiency and reliability of the system. Participating in reactive power compensation reduces the revenue generating active power from DG, thereby reducing DG owners profits. Payment for reactive power is therefore being looked at as a possibility in recent times. Optimal power factor (pf) of operation of DG becomes significant in this scenario. The study in this paper is presented in two parts. The first part proposes a novel method for determining optimal sizes and locations of distributed generation in a radial distribution network. The method proposed is based on the recent optimization algorithm, TeachingLearning-Based Optimization with Learning Enthusiasm Mechanism (LebTLBO). The effectiveness of the method has been compared with existing methods in the literature. The second part deals with the determination of optimal pf of operation of DG sources to minimize reactive power cost, reduce distribution losses and improve voltage stability. The approachs effectiveness has been tested with IEEE 33 and 69 bus radial distribution systems. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
Recently, intelligent transportations system (ITS) has gained significant internet due to the higher needs for road safety and competence in interconnected road network. As a vital portion of the ITS, traffic flow prediction (TFP) is offer support in several dimensions like routing, traffic congestion, and so on. To accomplish effective TFP outcomes, several predictive approaches have been devised namely statistics, machine learning (ML), and deep learning (DL). This study designs an optimal stacked sparse autoencoder based traffic flow prediction (OSSAE-TFP) model for ITS. The goal of the OSSAE-TFP technique is to determine the level of traffic flow in ITS. In addition, the presented OSSAE-TFP technique involves the traffic and weather data for TFP. Moreover, the SSAE based prediction model is designed for forecasting the traffic flow and the optimal hyperparameters of the SSAE model can be adjusted by the use of water wave optimization (WWO) technique. To showcase the enhanced predictive outcome of the OSSAE-TFP technique, a wide range of simulations was carried out on benchmark datasets and the results portrayed the supremacy of the OSSAE-TFP technique over the recent state of art methods. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.