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Ensuring Organizational Sustainability through HR Practices: Moderating Role of Leadership in the Banking Industry in the Context of SDGs
The study inspects the moderating role of leadership in the association between human resource (HR) practices and organizational sustainability, with a particular focus on Sustainable Development Goals (SDGs) 8 (Decent Work and Economic Growth) and 12 (Responsible Consumption and Production). It explores how leadership behaviors shape the effectiveness of HR practices in driving sustainability across economic, environmental, and social dimensions, while also situating these outcomes within the broader context of regional development and spatial planning. By analyzing the role of banks as institutional actors, the research highlights their contribution to financial inclusion, community well-being, and balanced urbanrural growth. A stratified random sample of 500 banking associates from urban, semi-urban, and rural branches was surveyed using a structured questionnaire, and data were analyzed through Structural Equation Modeling with SPSS and AMOS. HR practices, including recruitment, onboarding, performance management, compensation, and employee engagement, were assessed alongside leadership behaviors such as decision-making, resource allocation, empowerment, and vision. The findings indicate that leadership has a significant impact on the positive effects of HR practices on sustainability outcomes. In particular, leading by example and effective resource allocation emerge as strong moderators that advance SDG 8 and SDG 12. The findings underscore that sustainable HR leadership integration in banking not only improves organizational outcomes but also contributes to regional development and planning agendas by reinforcing equitable growth and sustainability across diverse spatial contexts. This study also situates banking institutions within the field of geography, planning, and development by showing how HR-leadership interactions contribute to territorial equity, financial inclusion, and spatial planning objectives. By linking organizational practices to regional sustainability trajectories, the findings highlight banks as critical institutional actors in advancing balanced urbanrural development. 2025, Green Publication. All rights reserved. -
Ensuring Equity and Mitigating Harm in AI (Fairness and Bias)
The rapid spread of Artificial Intelligence (AI) across sectors like healthcare, finance, education, law enforcement, and public administration has dramatically changed how decisions are made, services are delivered, and organizations function. AI holds incredible potential to improve human well-being and drive societal progress. Yet, alongside these opportunities come serious ethical concernsparticularly around fairness, bias, and the risk of reinforcing existing social inequalities. This chapter explores these challenges in depth, offering an interdisciplinary perspective on how bias emerges in AI systems. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Ensuring cinema's success and failing audience: Exploring dominant cinematic violence
Screen violence has steadily increased in Indian cinema and has become a commercially lucrative aesthetic. The more Indian cinema portrays violence, the more success it registers, breaking the previous record set by yet another violent crime movie. The intended meaning of these successful violent productions and the screen messages perceived by the audience seem to echo each other, reinforcing specific dominant cultural values. While recording commercial success, these films completely rewire the essential Indian cinematic aesthetics cultivated over a century. This paper is a narrative commentary on the increased violence in Indian cinema in the last six years and attempts to point out the lost significance of other film genres. The arguments presented are drawn from a visual content analysis of 50 commercially successful films produced between 2018-23. The paper attempts to problematize the shrinking diverse audience and the increasing monolithic audience looking for a one-time screen experience rather than appreciating the cinema possibilities as a mass-appealing medium. 2025 by IGI Global Scientific Publishing. -
Ensembled convolutional neural network for multi-class skin cancer detection
A skin cancer diagnosis is critically important in medical image processing. The role of dermoscopy and dermatologists is inevitable in skin cancer diagnosis. But, considering the time constraints on diagnosing patients on time, even medical experts need computer-assisted methods to automate the diagnosis process with a higher accuracy rate and with good performance. Such computer-assisted methods with induced artificial intelligence (AI) algorithms are gaining significance. The challenging task of medical image processing is finding benign/malignant pigmented skin lesions after the input image of patients. To identify this difference, AI-based classification algorithms shall be deployed. During the implementation of such algorithms, several performance aspects are evaluated. Once the best such algorithm is identified and evaluated for its performance attributes, it shall be deployed to assist dermatologists. This book chapter explains such a novel multiclass skin cancer classification algorithm. The proposed algorithm uses the best of the attributes and parameters of a deep convolutional neural network (CNN) to give the best-ever enactment among similar existing algorithms. The result achievement of the developed deep CNN based multi-class skin cancer classification algorithm (DCNN-MSCCA) is demonstrated using the HAM10000 dataset. To establish the significance of the developed algorithm, the performance parameters of the DCNN-MSCCA are compared with a few existing significant algorithms. The maximum accuracy of DCNN-MSCCA in predicting the exact multi-class skin cancer is 95.1%. This book chapter explains the implementation details of DCNN-MSCCA using python and libraries supporting CNN. 2024 River Publishers. -
Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation
The development of software involves expending a significant quantum of time, effort, cost, and other resources, and effort estimation is an important aspect. Though there are many software estimation models, risks are not adequately considered in the estimation process leading to wide gap between the estimated and actual efforts. Higher the level of accuracy of estimated effort, better would be the compliance of the software project in terms of completion within the budget and schedule. This study has been undertaken to integrate risk in effort estimation process so as to minimize the gap between the estimated and the actual efforts. This is achieved through consideration of risk score as an effort driver in the computation of effort estimates and formulating a machine learning model. It has been identified that risk score reveals feature importance and the predictive model with integration of risk score in the effort estimates indicated an enhanced fit. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ensemble Hybrid LSTM Architectures for Robust Multi-Currency Forex Forecasting
The analysis of financial time series presents a longlasting obstacle regarding currency exchange rate forecasting because volatility and nonlinearity and non-stationarity characterize currency markets. The research presents an ensemble forecasting system which combines various deep learning and hybrid predictive models such as LSTM and GRU-LSTM and CNN-LSTM and Attention-LSTM and XGBoost-LSTM for scalable integration. The ensemble methodology follows a dynamic weighted averaging technique which bases its priority on assigning weights through the reciprocal calculation of Mean Squared Errors from individual models to identify accurate forecasters. A representative study based on the EUR/USD exchange rate took place as part of extensive evaluations that spanned various currency pairs. The standalone XGBoost-LSTM model proved most effective in terms of MSE and R2 values at 0.000088 and 0.9778 respectively. The ensemble model proved to be highly robust and generalizable through its outcomes which produced an MSE of 0.000142 along with MAE of 0.009204 and R2 of 0.9643. The ensemble approach stands as an effective and reliable method to increase both stability and predictive power of forex forecasting systems. The conceptual structure offers sound potential applications for algorithmic trading as well as financial risk management and multi-currency strategic decision-making systems. 2025 IEEE. -
Ensemble Deep Learning for COVID-19 Detection Using Multi-Modal Medical Imaging
The COVID-19 pandemic has had a profound impact worldwide This work proposes a deep ensemble learning model incorporating multi-modal inputs, i.e., CT scans and Xrays, to classify the cases into COVID-19, Viral Pneumonia, or Normal. Employing an ensemble average voting approach from three different CNN models InceptionV3, DenseNet-169, and Xception the suggested methodology is highly accurate and reliable. Preprocessing methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) improve data quality, and Local Interpretable Model-Agnostic Explanations (LIME) allow interpretable prediction through identification of major image features driving classifications. The ensemble model suggested attains an accuracy of 99.64%, outperforming single models, with precision at 99.50%, recall at 99.73%, and an F1-score of 99.61%, which makes it very reliable for detecting COVID-19. Comparative analysis shows that our ensemble method performs better than individual CNN architectures, such as Xception (99.18%), ResNet101 (98.95%), and DenseNet201 (98.83%), which showcases its better diagnostic performance. 2025 IEEE. -
Ensemble Deep Learning Approach for Turbidity Prediction of Dooskal Lake Using Remote Sensing Data
The summer season in India is marked by a severe shortage of water, which poses significant challenges for daily usage and agricultural practices. With unpredictable weather patterns and irregular rainfall, it is crucial to monitor and maintain water bodies such as domestic ponds and lakes in urban areas to ensure they provide clean and safe water for regular use, free from industrial pollutants. In this research paper, we propose an innovative ensemble deep learning approach (e-DLA) that leverages deep learning models to predict the turbidity of Dooskal Lake, located in Telangana, India, using remote sensing data. The proposed approach utilizes various deep learning models, including bagging, boosting, and stacking, to analyze the complex relationships between remote sensing data and turbidity levels in the lake. The study aims to provide accurate and efficient predictions of turbidity levels, which can aid in the management and conservation of water resources in the region. Hyperparameter tuning is employed, and dynamic climatic features are extracted and integrated with the ensemble learning global protective intelligent algorithm to reveal the complex relationship between in situ and measured values of turbidity during the measuring timeline. The proposed approach provides accurate predictions of turbidity levels, enabling the implementation of effective control measures to maintain water quality standards. Experimental results demonstrate that the proposed approach significantly reduces prediction errors compared to existing deep learning models. Overall, this research highlights the potential of machine learning techniques in monitoring and maintaining water resources, particularly in urban areas, to support sustainable water management and usage, and addresses an urgent and pressing issue in India and around the world. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0
Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models. 2024 -
Enhancing Workplace Efficiency with The Implementation of the Internet of Things to Advance Human Resource Management Practices
Improving human resource management via the use of the Internet of Things (IoT) is the focus of this research. The primary objective is to enhance productivity in the workplace. The researchers utilized a mix of qualitative (describing) and quantitative (numbers-based) techniques to collect and analyses data. This research shows that key HR KPIs are positively affected by using IoT in HRM. Businesses utilizing IoT for real-time monitoring have better operations and more engaged employees. The study found that state-of-the-art technology, extensive training, and effective change management were needed to overcome people's security concerns and unwillingness to change. The Internet of Things may transform HRM and corporate operations, according to study. According to study, companies should invest in people-focused technologies and services. It emphasizes creating a workplace that embraces new technology while prioritizing security and privacy. In conclusion, the study's results may help organizations navigate HRM and the IoT's changing terrain. It suggests linking HR and technology to improve workplace flexibility and efficiency. 2024 IEEE. -
Enhancing worklife balance: Social support, resilience, and job satisfaction among Indian gazetted police officers
Police officers grapple with the challenge of balancing their professional obligations and personal lives. They endure 10- to 12-hour shifts and make split-second decisions that can have life-altering consequences for themselves and others. This job adds complexity, requiring them to manage time and effort. This study examines the mediating role of job satisfaction and resilience in the relationship between social support and the worklife balance of gazetted police officers. The proportional quota sampling method was used to collect data from 242 respondents across different ranks (senior, middle, and junior) in the state of Karnataka. Structural equation modeling analysis was done on the collected data using SPSS Amos. This study demonstrates that social support significantly enhances both job satisfaction and resilience, which, in turn, contribute to better worklife balance. The findings highlight the mediating roles of resilience and job satisfaction, reinforcing the idea that a strong support system fosters personal and professional well-being. To reduce workload and improve worklife balance, organizations can increase recruitment in line with UN recommendations and establish peer support programs, mindfulness-based interventions, and cognitive behavioral programs through police training academies. 2025 Taylor & Francis Group, LLC. -
Enhancing well-being: Exploring the impact of augmented reality and virtual reality
Virtual reality (VR) and augmented reality (AR) can revolutionize how individuals experience and perceive the world. Effective and engaging wellness practices are made possible by these technologies personalized, immersive experiences. The organization endeavors to foster empathy and understanding by attending to physical, mental, emotional, and social health. Nevertheless, ethical deliberations are of the utmost importance, including privacy, proper data handling, and secure data access. Education, support, and accessibility are critical determinants of user acceptance. Additional areas that warrant further investigation include treatment efficacy, diversity, long-term effects, and ongoing progress. A more inclusive, engaging, and productive approach to individual and communal health is anticipated due to the expanding use of AR and VR in well-being. 2024, IGI Global. All rights reserved. -
Enhancing Well- Being in the Digital Age in the VUCA and BANI World
In today's VUCA (Volatile, Uncertain, Complex, Ambiguous) and BANI (Brittle, Anxious, Nonlinear, Incomprehensible) reality, enhancing well-being in the digital age is crucial. Digital technology can both support and hinder mental health, making it essential to cultivate a balanced approach. Encouraging digital detoxes and mindfulness practices can reduce stress and promote emotional resilience. Leveraging online communities for support fosters connection, but awareness of the potential for digital addiction remains essential. Tools like mental health apps can provide resources, yet they should complement real-life interactions to ensure authenticity. Emphasizing digital literacy empowers individuals to navigate information critically, enhancing decision-making regarding their mental and emotional health. Finally, organizations must prioritize employee well-being by implementing flexible work arrangements, promoting open communication, and providing mental health resources. By consciously shaping our digital environments, we can enhance well-being and thrive in this complex world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and Learning Techniques
This research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Visual Passwords Using a Grid-Based Graphical Password Authentication to Mitigate Shoulder Surfing
Surfing Shoulder Surfing is a secret phrase-based attack which is a serious worry of protection in data security. Alphanumeric passwords are more helpless to attacks like shoulder surfing, dictionary attacks, etc., than graphical passwords. The creation of more muddled, challenging to-break passwords can be made simpler for clients with graphical authentication by consolidating the visuals and memory-based strategies like recall and recognition. In an imaged-based password, the user can choose pixels from the image to use as a secret key in the grid-based strategy, the user-selected image would show up on the screen with a framework overlay on it, and the client can pick explicit lattices to set their secret phrase. Besides, graphical passwords are powerless against shoulder surfing attacks, and due to this, clients are given a one-time made password via email. We investigated the limitations of image-based and grid-based authentication techniques and propose a grid-based graphical authentication system that addresses the limitations of image-based and grid-based techniques. The results of the grid-based graphical technique, as well as the image-based and grid-based approaches, have likewise been differentiated and analyzed. The convenience objective of our authentication system is to assist users in making better password selections, hence boosting security and broadening the usable password field. This method can be employed in many different contexts, such as forensic labs, banking, military, and other scenarios. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Enhancing Video Surveillance for Crime Detection Using Anomaly Detection Techniques
Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. The process of detecting similarities or data points that significantly depart from the norm or expected behavior of a given system is known as anomaly detection. Predictive maintenance, network intrusion detection, and fraud detection are just a few of the areas where anomaly detection is applied. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of experts to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. An anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. In this context, video surveillance refers to observing the scenes of improper human behaviors which are termed as real world anomalies. Depending on the availability of data sets, anomaly detection algorithms can be supervised, unsupervised, or semi- supervised. The quality of the data and the selection of the best algorithms determine how well anomaly detection techniques work. This paper proposes the use of anomaly detection techniques to enhance video surveillance systems for crime detection. By identifying unusual activities in surveillance footage, the system can alert authorities to potential criminal activity and improve overall security measures. The effectiveness of this approach is demonstrated through experiments and analysis of real-world surveillance data. 2025 Author(s). -
Enhancing User Control: A Reinforcement Learning Framework for Breaking Filter Bubbles in Recommender Systems
In an age of information overload, recommendation systems play an important role in providing personalized content to users. However, traditional recommendation systems often create filter bubbles, limiting the types of content users are exposed to. Based on the research presented in the article Breaking the Filter Bubble: A Reinforcement Learning Framework for Controllable Recommender Systems, this article proposes a new approach to further improve the controllability and diversity of recommendations. By using reinforcement learning techniques, the proposed framework aims to break the filter bubble by providing users with more diverse content recommendations while maintaining high recommendation accuracy. Extensive experiments on real-world datasets demonstrate the effectiveness of this approach in suppressing recommendation concentration and improving recommendation diversity. The results of this study contribute to the further development of controllable recommendation systems and provide insights into solving the filter bubble problem in recommendation systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Enhancing Transparency and Trust in Agrifood Supply Chains through Novel Blockchain-based Architecture
At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demandsupply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains. Copyright 2024 KSII. -
Enhancing Traffic Incident Management and Regulatory Compliance Using IoT and Itms: A Mumbai Traffic Police Case Study
In the rapidly urbanizing landscape of Mumbai, a megacity confronted with significant traffic management and law enforcement challenges, the deployment of an advanced city surveillance system represents a transformative approach to urban governance. This paper examines the integration of over 11,000 CCTV cameras into the Mumbai Traffic Police's operational framework, covering an area of 438 square kilometers encompassing 41 traffic divisions and 94 police stations. Since its inception in 2016, the system has been pivotal in enhancing safety, order, and mobility within the city, especially amid obstacles such as ongoing infrastructure projects, traffic congestion, accidents, and natural disasters. Central to this study is the analysis of the Mumbai City Surveillance System Project (MCSP), which leverages CCTV technology to generate and classify Incident Reports (IR) based on severity, ranging from minor disruptions to significant emergencies. The period from October 2021 to 2023 saw a marked increase in IR generation, from 742 reports in 2021 to 10,392 in 2022 and 9,639 in 2023, indicating the system's growing efficacy in real-time traffic management and incident response.This paper further explores the cutting-edge integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies within the MCSP framework, highlighting the role of computational intelligence in enhancing the capabilities of Intelligent Transportation Systems (ITS). By employing AI-driven predictive analytics, the system effectively anticipates traffic conditions based on diverse variables such as traffic flow, vehicle speed, and weather, thereby optimizing traffic management strategies.The findings underscore the significant impact of AI and IoT technologies in redefining urban transportation networks, demonstrating improved efficiency, safety, and resilience in the face of Mumbai's complex transportation challenges. This study contributes to the discourse on smart city initiatives, offering insights into the role of advanced computational technologies in facilitating intelligent transportation solutions and shaping the future of urban living. 2024 IEEE. -
Enhancing Time Series Forecasting in Low-Liquidity Markets Using Generative Adversarial Networks
Financial assets that are low liquidity are very difficult to forecast as they are sparsely traded, their volatility is not regular, and scarce historic evidence exists. This paper will explore the hypothesis of whether in this kind of limited environment, generative models can enhance the effectiveness of forecasting. A dual model framework is constructed which contrasts a normal Long Short Term Memory (LSTM) network with TimeGAN based synthetic data augmentation method in 60-day long-range forecasting of the TRY/USD exchange rate. The methodology consists in the training of an LSTM model on real historical sequences and the improvement with TimeGAN generated synthetic sequences with a maintained temporal structure. It has been shown that TimeGAN has a significant effect on the accuracy of the forecasts, the RMSE decreased to 0.0002 by approximately fifty percent, and the R2 grew to 0.9921 by approximately fifty percent. The results suggest that augmentation through GAN enhances generalization of models in thin and dynamic markets. The most important contributions include implementation of TimeGAN to low-liquidity FX forecasting, the assessment of the effects of synthetic data on forecast accuracy and the empirical benchmark of LSTM and TimeGAN in low-volume finance. 2025 IEEE.