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Adapting Case Study Pedagogy for Non-Residential Business Schools: Strategies for Implementation
Case study pedagogy is widely recognized as a powerful teaching approach in business education programs. However, its implementation in non-residential business schools poses distinct challenges. Optimizing case study pedagogy to the unique needs and circumstances of non-residential students necessitates a specific strategy. This chapter delves into various strategies essential for the effective implementation of case study pedagogy in non-residential business schools. First, an overview of 2024 by IGI Global. All rights reserved. -
Adapting Employee Engagement Strategies Amid Crisis: Insights from the COVID-19 Pandemic
Crises are unpredictable events that have the potential to strike at any moment, causing significant disruptions to work, daily routines, and the normal course of life. The COVID-19 Pandemic served as a facilitator for transformative changes in the way we work, shifting to an era of remote and flexible work arrangements across industries. This crisis underlined the importance of employee engagement and organizational culture-building in navigating unforeseen situations. As organizations prepare for the future, it becomes crucial to anticipate and adapt to potential crises that may arise. The effect of the pandemic varied from industry to industry. When the technology industry worked towards creating a virtual workspace, the production industry strived to continue production without disruption. However, irrespective of the industry, HR teams across the board were dedicated to identifying and addressing the challenges posed by the crisis. They have worked tirelessly to ensure employee engagement remains a priority. This qualitative study explores the challenges encountered by HR teams during the pandemic and explores the strategies and policies they adopted to foster employee engagement. The data was collected through an in-depth interview with 39 HR Practitioners from different industries. The significant challenges included the need to cultivate a sense of community, navigate muddled up HR processes, sustain productivity amid disruptions, and prioritize employee wellness. To provide a comprehensive analysis, this study examined industry-specific approaches, employing within-case analysis to understand key strategies in communication, rewards and recognition, employee benefits, wellness initiatives, and fostering an enjoyable virtual workplace. This study offers a forward-looking perspective and serves as a guide for organizations aiming to thrive in times of uncertainty, ensuring that employee engagement remains a strategic priority regardless of the crisis at hand. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Adapting Employee Engagement Strategies Amid Crisis: Insights from the COVID-19 Pandemic
Crises are unpredictable events that have the potential to strike at any moment, causing significant disruptions to work, daily routines, and the normal course of life. The COVID-19 Pandemic served as a facilitator for transformative changes in the way we work, shifting to an era of remote and flexible work arrangements across industries. This crisis underlined the importance of employee engagement and organizational culture-building in navigating unforeseen situations. As organizations prepare for the future, it becomes crucial to anticipate and adapt to potential crises that may arise. The effect of the pandemic varied from industry to industry. When the technology industry worked towards creating a virtual workspace, the production industry strived to continue production without disruption. However, irrespective of the industry, HR teams across the board were dedicated to identifying and addressing the challenges posed by the crisis. They have worked tirelessly to ensure employee engagement remains a priority. This qualitative study explores the challenges encountered by HR teams during the pandemic and explores the strategies and policies they adopted to foster employee engagement. The data was collected through an in-depth interview with 39 HR Practitioners from different industries. The significant challenges included the need to cultivate a sense of community, navigate muddled up HR processes, sustain productivity amid disruptions, and prioritize employee wellness. To provide a comprehensive analysis, this study examined industry-specific approaches, employing within-case analysis to understand key strategies in communication, rewards and recognition, employee benefits, wellness initiatives, and fostering an enjoyable virtual workplace. This study offers a forward-looking perspective and serves as a guide for organizations aiming to thrive in times of uncertainty, ensuring that employee engagement remains a strategic priority regardless of the crisis at hand. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Adapting palates: A mapping of food neophobia and neophilia in the shift towards sustainable food consumption
This research explores how two different personality traits-neophilia and neophobia-affect people's eating habits and preferences in the context of sustainable gastronomy tourism. Neophilia, which indicates an openness to trying new culinary experiences, contrasts with neophobia, which is defined as a fear of new foods. Data was collected from 234 gastronomy tourists in Bangalore to examine these dynamics. Smart PLS-SEM 4 was utilized for data analysis. The survey investigated the attitudes and behaviours of participants regarding sustainable food practices that they encountered while engaging in gastronomy tourism. The results show that food neophobia significantly improves people's perceptions of food quality, which further had a statistically significant favourable influence on sustainable consumption; it had no significant effect on post-consumption behaviour. The study highlights how vital gastronomy is to improving experiences, preserving local identity, and drawing tourists-particularly in the rapidly growing category of culinary tourism. 2024, IGI Global. All rights reserved. -
Adaptive AI: Transforming Natural Language Processing and Industry Applications
Adaptive Artificial Intelligence (AI) is an innovative development in the domain of intelligent systems because machines have attained the ability to independently learn, modify, and optimize their operations based on experience and other real-time information. While traditional artificial intelligence (AI) is programmed to follow a certain set of algorithms and rules step-by-step, adaptive AI systems have a level of independence that affords them the flexibility to change their actions. As a result, healthcare, finance, education, manufacturing, and many other industries can now employ AI, which was formerly not possible due to its inflexible nature, for enhanced and efficient decision-making, improved consumer practice, and effective working processes. There is significant scope for adaptive AI to transform Natural Language Processing (NLP) as well as business practices and solve complex problems. On the other hand, there is also the advancement of dependency on data, algorithmic perception, and ethical inquiries concerning the application of these technologies which pose a challenge. This proposed chapter emphasizes the techniques and applications of Adaptive AI in NLP and the ethical challenges that make it authoritative to adopt responsible development of artificial intelligence. 2025 Scrivener Publishing LLC. -
Adaptive algorithms in smart antenna beamformation for wireless communication
The challenges for today's wireless communication technology are increased data rates, channel capacity and spectrum efficiency with reduced interference. The adaptive antenna array is capable of adapting to the varying signal environments automatically and forms beams in the directions of the desired signals by steering nulls in the directions of interfering signals. Therefore smart antenna is the best solution to overcome the above mentioned challenges. Smart antennas uses advanced digital signal processing algorithms to enhance the detection of desired users in an interfering environment through spatial filtering. In this paper we will discuss the influence of Least Mean Squares (LMS), Recursive Least Squares (RLS) and Normalized Least Mean Square (NLMS) algorithms in adaptive beamforming. The simulations used for the study are carried out using MATLAB R2013a. 2016 IEEE. -
Adaptive artificial bee colony (aabc)-based malignancy pre-diagnosis
Lung cancer is one of the leading causes of death. The survival rate of the patients diagnosed with lung cancer depends on the stage of the detection and the timely prognosis. Hence, early detection of anomalous malignant cells is needed for pre-diagnosis of lung cancer as it plays a major role in the prognosis and treatment. In this work, an innovative pre-diagnosis approach is suggested, wherein the size of the dataset comprising risk factors and symptoms is considerably decreased and optimized by means of an Adaptive Artificial Bee Colony (AABC) algorithm. Subsequently, the optimized dataset is fed to the Feed-Forward Back-Propagation Neural Network (FFBNN) to perform the training task. For the testing, supplementary data is furnished to well-guided FFBNN-AABC to authenticate whether the supplied investigational data is competent to effectively forecast the lung disorder or not. The results obtained show a considerable improvement in the classification performance compared to other approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Adaptive Communication Protocols for Manager-Worker Small LLM Multi-Agent Systems in Resource-Constrained Environments
The proliferation of Internet of Things (IoT) and edge computing paradigms has necessitated the deployment of intelligent multi-agent systems in resource-constrained environments, where traditional communication protocols fail to optimize bandwidth utilization and energy consumption. This paper presents a novel adaptive communication framework specifically designed for manager-worker small Large Language Models (LLMs) multi-agent systems operating under stringent computational, memory, and energy constraints. Our approach integrates three synergistic innovations: (1) a lightweight semantic filtering module employing knowledge-distilled small LLMs (DistilBERT 66M parameters, TinyBERT 4.4M parameters) for real-time extraction of task-relevant information with minimal computational overhead, (2) a dynamic hierarchical coordination scheme enabling runtime role reassignment based on evolving task complexity and resource availability, and (3) an adaptive topology control mechanism leveraging algebraic connectivity measures to optimize network robustness while minimizing communication overhead. Comprehensive simulation-based evaluation across five distinct IoT deployment scenarios demonstrates substantial improvements in communication efficiency, achieving an average token reduction of 5 7. 4 % (range: 5 4. 1 - 5 9. 4 %) while maintaining task completion rates within 8.3 percentage points of baseline performance. The framework exhibits superior coordination quality improvements of 6.0 %, coupled with significant resource optimization including 5 1. 2 % CPU usage reduction and 50.4 % energy savings, validating its practical suitability for edge computing deployments in resource-critical applications. 2025 IEEE. -
Adaptive consumer psychology navigating trust and scepticism in automated retail experiences
The rapid deployment of automated retail technologies has brought both opportunities and challenges for consumer acceptance. Trust is an important factor for benefits determination of the automation systems. Sometimes Skepticism often acts as a barrier to adoption. This chapter enlightens into the psychology of consumer trust, perceived security, reliability, and transparency in automated systems. The research identifies key psychological triggers lead to skepticism in such a way that the data misuse or system failures and solution mitigation. The chapter highlights the role of adaptive design in consumer hesitations and positive interactions. Using empirical evidence and practical approach emphasizes technological efficiency balance with emotional reassurance and consumer comfort in automated environments. 2026, IGI Global Scientific Publishing. -
Adaptive Fault-Tolerant System and Optimal Power Allocation for Smart Vehicles in Smart Cities Using Controller Area Network
Nowadays, the power consumption and dependable repeated data collection are causing the main issue for fault or collision in controller area network (CAN), which has a great impact for designing autonomous vehicle in smart cities. Whenever a smart vehicle is designed with several sensor nodes, Internet of Things (IoT) modules are linked through CAN for reliable transmission of a message for avoiding collision, but it is failed in communication due to delay and collision in communication of message frame from a source node to the destination. Generally, the emerging role of IoT and vehicles has undoubtedly brought a new path for tomorrow's cities. The method proposed in this paper is used to gain fault-tolerant capability through Probabilistic Automatic Repeat Request (PARQ) and also Probabilistic Automatic Repeat Request (PARQ) with Fault Impact (PARQ-FI), in addition to providing optimal power allocation in CAN sensor nodes for enhancing the performance of the process and also significantly acting a role for making future smart cities. Several message frames are needed to be retransmitted on PARQ and fault impact (PARQ-FI) calculates the message with a response probability of each node. 2021 Anil Kumar Biswal et al. -
Adaptive Fuzzy Heuristic Algorithm for Dynamic Data Mining in IoT Integrated Big Data Environments
The explosion of Internet of Things (IoT) devices has created enormous amounts of real-time data, requiring sophisticated Data Mining Methods (DMT) that can rapidly extract valuable insights. Managing the computational complexity of processing high data volumes, integrating various IoT data formats, and ensuring that the system can scale are among the most significant issues. Fuzzy Dynamic Adaptive Classifier Optimization Analysis (FDACOA) is a method that has been suggested as an approach to the difficulties caused by changes in data patterns, processing in real-time, and data heterogeneity. By incorporating Adaptive Fuzzy Logic (AFL) and heuristic optimization, FDACOA enhances data classification accuracy and efficiency while simultaneously assuring that the algorithm can adapt to changes in data streams. This adaptability is crucial in IoT applications, where data fluctuation might affect analysis quality. FDACOA uses dynamic adaptation to alter classifier parameters based on real-time feedback to improve prediction accuracy and reduce computing costs. An optimization layer fine-tunes fuzzy rules and membership functions to optimize performance across data situations. Simulation analyses proved the algorithm's capacity to classify with high accuracy and low computational cost. Smart healthcare, predictive maintenance in industrial IoT, and intelligent transportation systems use FDACOA for real-time decision-making and data-driven insights. FDACOA is a viable approach for dynamic data mining in IoT-enabled big data contexts because of its faster, more accurate, and more adaptable simulation results. 2025, Research Expansion Alliance (REA). All rights reserved. -
Adaptive Grasshopper Optimization Algorithm for Multi-Objective Dynamic Optimal Power Flow in Renewable Energy Integrated Microgrid
Global warming has prompted several governments to adopt more sustainable policies in all areas. Incorporating renewable energy sources (RES) and adopting electric vehicles (EVs) are examples of such practises. Today's electrical distribution networks (EDNs) are becoming more reliable microgrids (MG) that can operate grid-connected or self-healing. As a result, the fluctuating nature of RES and EVs has raised numerous technical and economic concerns. This research proposes a novel multi-objective dynamic optimum power flow (OPF) addressing total load dispatch cost minimization and network security margin maximisation for various load profiles. A composite load model is proposed considering residential, industrial, commercial, EVs, agricultural loads. The proposed optimization issue is tackled using an adaptive grasshopper optimization algorithm (AGOA), a metaheuristic grasshopper optimization technique with adaptive control parameter (AGOA). A modified IEEE 33-bus benchmark test system with PV units and reactive power compensation devices is used for simulation over 24-hour horizon. The suggested AGOA's computing efficiency is compared for two scenarios. By combining good exploration and exploitation features with adaptive regulating variables, the AGOA outperformed in terms of global optima. Also, the techno-economics of MG operation and control are improved significantly. In scenario 1, the network is configured in a radial topology, with average operational costs, distribution losses, voltage variation, and transmission loadability of 1117.72 $/h, 82.4803 kW, 0.0058 p.u., and 0.7910 p.u., respectively, over a 24-hour period. In scenario 2, the network is run as a meshed network, with network performance of 1113.36 $/h, 43.15 kW, 0.0019 p.u., and 0.8524 p.u., respectively. This suggests that switching from radial to meshed configuration can result in lower losses, a better voltage profile, and increased loadability, as well as the applicability of the suggested methodology for managing uncertainty in modern EDNs. 2022. All Rights Reserved. -
Adaptive Hybrid Multi-Objective Evolutionary Algorithm for Wireless Sensor Network Optimization: A Comprehensive Framework Integrating Opposition-Based Learning and Levy Flight Strategies
Wireless sensor networks constitute a foundational technology for ubiquitous monitoring and data acquisition across diverse application domains ranging from environmental surveillance to critical infrastructure management. The operational efficacy and longevity of these networks critically depend on strategic configuration of multiple design parameters including field coverage, sensors per cluster in-charge, sensor out-of-range error, overlaps per cluster in-charge, and network energy consumption. These objectives exhibit inherent trade-offs, rendering the optimization problem a complex multi-objective challenge characterized by conflicting criteria and high-dimensional search spaces. This research presents a novel adaptive hybrid multi-objective evolutionary algorithm that synergistically integrates opposition-based learning for enhanced population diversity and initialization, Levy Flight mutation for effective escape from local optima, and adaptive operator selection for dynamic adjustment of genetic operator probabilities. We conducted exhaustive empirical evaluation comprising independent runs with individuals evolved over multiple generations, benchmarking the proposed algorithm against three state-of-the-art approaches. Performance metrics were computed using global normalization with respect to theoretical problem bounds to ensure measurement validity and cross-algorithm comparability. Statistical analysis including non-parametric rank tests, pairwise comparisons, and effect size quantification confirm the proposed algorithm achieves statistically significant improvements with very large practical significance. The algorithm demonstrates superior convergence characteristics, solution diversity, and Pareto front quality, establishing a robust framework for automated wireless sensor network configuration in resource-constrained environments. 2026 The Author(s). Engineering Reports published by John Wiley & Sons Ltd. -
Adaptive Mesh Networking Protocol for Self-Healing Electrochemical Sensor Networks in Environmental Monitoring Applications
Sensor networks for environmental monitoring must be robust, flexible, and long-lasting, and comprehensive reviews and evaluations of adaptive mesh networking protocols for self-healing to enable autonomous operation under challenging environmental conditions are needed. The purpose of this study was to conduct an extensive review and assessment of adaptive mesh networking protocols for the self-healing of electrochemical sensor networks used in environmental monitoring. The Adaptive Mesh Networking Protocols enable the distributed autonomous sensors (distributed over vast areas or through obstructions) to dynamically route their collected data, recover when nodes fail, and extend their life (in real-time). In evaluating adaptive mesh networking protocols, we reviewed several key features, including self-healing mechanisms, adaptive routing algorithms (including their mathematical representations), methods for achieving energy efficiency, and mechanisms for securing data collection from autonomous sensor networks. Our simulation results show that our proposed adaptive mesh networking protocol achieves greater than 98% packet delivery success, even with up to 30% of nodes lost. Furthermore, we have shown that our approach can reduce the energy consumption of autonomous sensors by up to 87.5% compared to existing non-adaptive approaches. Our demonstration of real-time monitoring dashboards and a comprehensive performance analysis of the autonomous sensor networks demonstrates the feasibility of implementing adaptive mesh networking protocols into large-scale environmental monitoring projects. A significant area of focus for future research will be sensor-level self-correction to address bio-fouling remediation. 2026 Taylor & Francis Group, LLC. -
Adaptive optimization with reinforcement learning for high utility itemset extraction
Extraction of High Utility Itemsets (HUI) plays a vital role in data mining that comprises several techniques developed to address it efficiently. However, when dealing with large itemsets and diverse items in a dataset, the problem's search space becomes notably complex and expansive. This makes the task of identifying HUIs more computationally expensive and time-consuming. In this paper, a novel Optimized Coverage list unit utilities-based High Utility Itemset (OCHUI) extraction approach is introduced for High Utility Itemset extraction. The extraction of high utility patterns and the extraction of qualified high utility itemsets are the two main processes in the suggested method. In the first step, high utility patterns are identified by mining metrics such as Redefined transaction-weighted utility, positive and negative Unit profit, Purchase quantity, and Coverage (RUPC) from the dataset. In the second step, qualified high utility itemsets are obtained optimally using an adaptive optimization algorithm called Cuckoo search Assisted Ant colony Optimization with Reinforcement Learning (CAAO-RL) is proposed. The Reinforcement Learning (RL) uses the On and Off policy method to intelligently leverage the tuning parameter of optimization. The RUPC model obtained the pattern score of 13600, runtime rate of 10.256 s and memory usage of 198 MB, respectively. 2025 Elsevier B.V. -
Adaptive Risk-Aware Ride Assignment (ARARA) Algorithm to Improve Efficiency to Lower Cancellation Rates in Bengaluru
Ride cancellations on urban mobility platforms like Rapido, OLA, Uber and other service provide platforms are negatively impacting user experience, driver earnings, and platform efficiency due to high cancellation of rides. This study addresses the challenge by developing a machine learning based adaptive userride matching algorithm that is trained on real world ride dataset from Bengaluru. The dataset includes features such as ride time, source, destination, distance, fare, payment method, and ride status. Through data preprocessing and feature engineering, key patterns influencing ride cancellations are identified. A classification model is developed to predict the likelihood of cancellation before ride assignment by using few Machine learning models among various model XGBoost and Logistic Regression outperformed with nearly 9 0% accuracy. Later to enhance the performance in allocation based on cancellation prediction the ARARA algorithm suggests that reallocates rides dynamically based on cancellation risk using inference and assignment Algorithm. Experimental results shows that how to reduce cancellation rates and improved accuracy by choosing best allocation based on top three best captains for allocation to optimize chances of cancellation. This framework can be integrated by ride platforms to enhance service reliability and optimize fleet efficiency. 2025 IEEE. -
Adaptive uplink scheduling model for WiMAX network using evolutionary computing model
The increased usage of smart phones has led to increase usage an internet based application services. These application requires different quality of service (QoS) and bandwidth requirement. WiMAX is an efficient network to provision high bandwidth connectivity and coverage to end user. To meet QoS requirement the exiting model used adaptive model selection scheme. However, these model induce bandwidth wastage as it does not considers any feedback information for scheduling. This work present an Adaptive Uplink Scheduling (AUS) by optimizing MAC layer using Multi-Objective Genetic Algorithm (MOGA). The MAC scheduler use feedback information from both physical layer and application layer. Further, to meet QoS requirement of application and utilize bandwidth efficiently this paper presented an adaptive modulation selection scheme based on user application requirement using MOGA. Our model provides application level based QoS provisioning for WiMAX network. Experiment are conducted to evaluate performance of AUS over exiting model. The overall result attained shows AUS model attain good performance in term of throughput, successful packet transmission and packet collision. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
AdaptiveNet: A Novel Architecture for Reducing Computation Complexity to Fake Review Classification
The exponential rise of e-commerce platforms has resulted in a dramatic increase in online reviews, which creates a challenge in distinguishing fake reviews that erode consumer confidence and harm commerce ecosystems. Traditional approaches for fake review detection employ computationally expensive deep learning networks which are resource-intensive and difficult to use in practice. In this paper, we describe AdaptiveNet, a new lightweight neural architecture that achieves fake review detection with much lower computational resources while maintaining a higher detection and classification precision. The model proposed in this paper is based on three original innovations: a Multi-Scale Semantic Fusion (MSSF) layer for hierarchical feature extraction, Dynamic Attention Scaling (DAS) with complexity measure attention, and Adaptive Parameter Sharing (APS) context-gated networks. With thorough evaluation on Amazon, Yelp, and TripAdvisor datasets of reviews totalling 1.2 million reviews, AdaptiveNet attains 94.8% accuracy while achieving 65% computational overhead in comparison to traditional models. The architecture outperformed all other state-of-the-art models, BERT-base (92.1%), RoBERTa (91.8%), and other more recent efficient models, requiring 70% lower parameters and 60% lower energy consumption. This work markedly advances the other efficient deep learning architectures for text classification and allows for the practical implementation of fake review detection systems in resource-limited settings as process innovation. 2026 by the authors. -
Addiction treatment in India: Legal, ethical and professional concerns reported in the media
As per the Magnitude of Substance Use in India 2019 survey report, over 57 million of the Indian population is in need of professional help for alcohol use disorders and around 7.7 million for opioid use disorders. The increasing demand for addiction treatment services in India calls for professionalising every aspect of the field. Frequent human rights violations and various unethical practices in Indian addiction treatment facilities have been reported in the mass media. This study is a content analysis of newspaper reports from January 1, 2016 to December 31, 2019 looking into legal, ethical and professional concerns regarding the treatment of substance use disorders in India. The content analysis revealed various human rights violations, the use of improper treatment modalities, the lack of basic facilities at treatment settings, and the presence of unqualified professionals in practice. Indian Journal of Medical Ethics 2021.