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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 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 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 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. -
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. -
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. -
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 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 robust and secure supply chain: Deploying blockchain
Transparency, visibility, security, source-to-store traceability, and rising customer expectation are the critical points in the retail supply chain. The global supply chain involves a nexus of manufacturers and suppliers who urge for a robust network addressing the above challenges in the supply chain. A better provenance tool can benefit retailers, as customers are more concerned about the retail journey of the product start from its origin. Within the small span since its inception, blockchain has revolutionized the businesses and shown promising result in reshaping the supply chain. Blockchain in retail can provide evidence for the authenticity of product, tacking details for reliable retail delivery and enriching customer experience through product provenance. This chapter aims to explain to retailers the challenges, opportunities, and potential application of blockchain in the retail supply chain. 2024, IGI Global. All rights reserved. -
Enterococcus faecalis CGz3 alleviating steatosis via BSH-mediated modulation in HepG2 cell-lines
The study aimed to evaluate the therapeutic potential of bile salt hydrolase (BSH)-producing probiotic Enterococcus faecalis CGz3 in alleviating steatosis in HepG2 hepatocarcinoma cells, with non-alcoholic fatty liver disease (NAFLD) induced by cholesterol and oleic acid (OA), focusing on its effects on lipid accumulation, metabolic gene expression and, inflammatory pathways. HepG2 cells were treated with cholesterol and OA to induce lipid accumulation, mimicking non-alcoholic fatty liver disease (NAFLD) conditions. Cells were then incubated with E. faecalis CGz3 for 6 hours at 37C and 5% CO2. Lipid levels were quantified using Oil Red O staining and cholesterol uptake assays, while gene expression of lipogenic, inflammatory and metabolic markers was assessed via quantitative real-time polymerase chain reaction (qRT-PCR). Treatment with E. faecalis CGz3 significantly reduced lipid accumulation from 42.961.35 mg/mL in NAFLD-induced cells to 29.731.26 mg/mL. It down regulated lipogenic genes (SREBP-1c, FAS and ACC) and inflammatory markers (TNF-?, IL-6, CRP, TLR4, TLR9, NF-?B, JNK, ERK) while upregulating PPAR? and AMPK, promoting fatty acid oxidation. No significant cytotoxicity was observed at 6 hours, though prolonged exposure (1224 hours) reduced cell viability. This study introduces E. faecalis CGz3, a novel BSH-producing probiotic isolated from chicken gizzard, as a promising candidate for NAFLD intervention. Its selective modulation of lipid metabolism and inflammation via BSH activity offers a new perspective on probiotic-based therapies for NAFLD, warranting further in vivo and clinical exploration. 2025, World Researchers Associations. All rights reserved. -
Enterococcus species and their probiotic potential: Current status and future prospects
Probiotics are described as live microbes that, once consumed in sufficient quantities, provide a health advantage to the host. A rising number of research works have verified the health benefits of probiotics. Enterococci are common bacteria that may be found almost anywhere. For their opportunistic pathogenicity, Enterococci have been associated with numerous nosocomial infections resulting from resistance to antibiotics and the existence of other virulence factors, notably the development of vancomycin-resistant Enterococci. However, some Enterococcal strains such as E. faecium and E. faecalis strains are being utilized as probiotics and are widely marketed, usually in the form of pharmaceutical solutions. Enterococcus spp. based probiotics are used to treat irritable bowel syndrome, infectious diarrhea, and antibiotic-associated diarrhea, along with decreasing cholesterol levels and enhancing host immunity. To be used as probiotics in the future, Enterococcal strains must be properly defined and thoroughly evaluated in terms of safety and can be beneficial. Here, in this work, we have reviewed various aspects of Enterococcus spp. pertaining to its possibility of being utilized as a probiotic strain. 2023 Krishna, et al. -
Enterpreneurial orientation and the management grid: A roadmap for the enterpreneurial journey /
Asian Journal Of Management, Vol.7, Issue 4, ISSN: 0976-495X (Print), 2321-5763 (Online). -
Entomotoxic proteins of Beauveria bassiana Bals. (Vuil.) and their virulence against two cotton insect pests
Entomopathogenic fungi are widely used as biocontrol agents against several agricultural pests. Among them, Beauveria bassiana is considered the important one against insect and other arthropod pests. The entomotoxic proteins of B. bassiana were extracted by Sephadex G-25 column, and fractionated using HPLC (BBI, BBII and BBIII) and tested against two hemipteran insect pests i.e., Dysdercus cingulatus Fab. and Phenacoccus solenopsis Tinsely (Hemiptera: Pseudococcidae). Results indicated that protein content was higher in fraction BBII than BBI and BBIII. The vibration frequency in FT-IR obtained with a range of 1650 to 1580 cm?1. Bioassays of fractions (I, II and III) reveal that BBII was highly virulent against third nymphal instar of D. cingulatus (LC50 = 800.2 ppm) and adults of P. solenopsis adult (LC50 = 713.3 ppm). Considering the high virulence of BBII subjected to SDS-PAGE, HPLC and MALDI-TOF analyses. Analyses reveals the presence of 174 kDa and designated as BBF2. These results concluded that the entomotoxic protein of B. bassiana can be utilized for management of these investigated hemipreran pests. Further investigations are necessary for the field application of this entomotoxin against these pests or other insect pests. These results also could be helpful for establishing novel biotechnological uses for this fungus. 2021 The Authors -
Entrepreneurial Attitude and Entrepreneurial Intentions of Female Engineering Students: Mediating Roles of Passion and Creativity
Entrepreneurship holds a crucial function in addressing societal and economic issues like joblessness and inequalities between different regions. Acknowledging its significance, government officials and educational institutions exert considerable energy towards nurturing individuals into entrepreneurs. Multiple elements influence a person's path to becoming an entrepreneur. This research seeks to examine how one's entrepreneurial attitude (EA) impacts one's drive to become an entrepreneur, with passion and creativity serving as an intermediary in this connection. The research is explanatory and employs a survey-based approach. The findings convey that entrepreneurial attitude significantly influences the determination of female engineering students to pursue entrepreneurship. The study highlights the mediating roles of passion and creativity in the relationship between entrepreneurial attitude and intentions. While passion positively mediated the relationship, creativity had a negative mediating effect. 2024, Institute of Economic Sciences. All rights reserved. -
Entrepreneurial challenges of transgender entrepreneurs in India
Social exclusion has impeded transgender individuals to enter mainstream society and curbing them to start a business venture. Sporadic transgender individuals have paved their way to start the business venture. This study aims to explore the entrepreneurial challenges faced by transgender entrepreneurs. Twenty transgender entrepreneurs who have relinquished begging and commercial sex work were interviewed. The grounded theory analysis has revealed six significant categories: financial resources, competitors, human resources, marketing issues, natural calamities, and transphobia. The participants expressed that transphobia, and financial resources were highly challenging to start a business venture. These findings extend our understanding of their challenges beyond the current knowledge of cisgender entrepreneurs. Finally, the limitation of the study is enunciated. Copyright 2025 Inderscience Enterprises Ltd. -
Entrepreneurial challenges of transgender entrepreneurs in India
Social exclusion has impeded transgender individuals to enter mainstream society and curbing them to start a business venture. Sporadic transgender individuals have paved their way to start the business venture. This study aims to explore the entrepreneurial challenges faced by transgender entrepreneurs. Twenty transgender entrepreneurs who have relinquished begging and commercial sex work were interviewed. The grounded theory analysis has revealed six significant categories: financial resources, competitors, human resources, marketing issues, natural calamities, and transphobia. The participants expressed that transphobia, and financial resources were highly challenging to start a business venture. These findings extend our understanding of their challenges beyond the current knowledge of cisgender entrepreneurs. Finally, the limitation of the study is enunciated. Copyright 2025 Inderscience Enterprises Ltd. -
Entrepreneurial journey of Rajat: from coffee to confidence
Learning outcomes The case was developed to explore principles of Effectuation, the traits, and external challenges faced by the entrepreneurs in small business. The main outcomes of this case are to: Compare and contrast the principles of effectuation, opportunity cost-based theory, and social capital theory, given the challenges faced by the protagonist. Critically analyze the risks associated with businesses and the factors that motivate entrepreneurs to take that risk. Critically evaluate which entrepreneurial traits were most crucial to Rajats journey, and contrast them with the traits highlighted in the literature on entrepreneurs in emerging markets. Create key strategic directions the Protagonist could explore after getting permission to start. Develop a business model canvas and a comprehensive business plan that would help the protagonist to pitch for his caf The business plan should include market analysis, financial planning, operations, and risk management. Case overview/synopsis The case explored the journey of a young entrepreneur and the challenges faced throughout his journey. Rajat started his career as an employee in a bakery and dreamt of having his own business one day. Initially, Rajat faced various challenges, which offered him a very good learning experience and shaped his entrepreneurial traits. Just before COVID-19, he was successful in opening his bakery near the University. The business failed miserably as the model was not effective during the lockdown period imposed due to the pandemic. He lost not only the money invested but also the trust of his close social network. He went back to his job and continued there for a significant period of time. He learnt more about the tricks of the trade and the market while in a job. It took nine years to gather the courage to venture once again into the uncertainty of an entrepreneurial journey. Never giving up, persistence, growth mindset, customer centricity, and pleasing personality were some of the traits that he had developed over a period of time. He looked forward to opening his cafinside the Universitys new campus. His proposal was initially declined by the University management when he approached them for the first time. This did not discourage him, and he continued to believe in his ability and instincts. He decided to approach the management once again to start his cafin the new campus of Yeshwanthpur. This time, it was not a denial (at least he thought so), but also not a very clear yes. Rajat now found himself in a dilemma: should he approach the management once again, possibly with a stronger business plan, though this might again involve seeking financial help from his family? The other two options were to continue at his current job till he gets a better opportunity to shift, and the last option was to return to his hometown to help his father in the catering business, a less trendy but potentially stable one. Complexity academic level This case is intended for the students of Entrepreneurship and Strategy in undergraduate and postgraduate courses. This case demonstrated how persistence and self-motivation enable people to become successful entrepreneurs. The case could also be used for understanding entrepreneurship challenges and the traits of an entrepreneur. Supplementary material Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship. 2025 Emerald Publishing Limited -
Entrepreneurship Education: Experiments with Curriculum, Pedagogy and Target Groups
The book provides an overview of developments in the field of entrepreneurship education, with special reference to global perspectives on innovations and best practices, as well as research in the emerging economy context. It focuses on various experiments in curriculum design, review and reform in addition to the innovative processes adopted for developing new content for entrepreneurship courses, in many cases with an assessment of their impact on students' entrepreneurial performance. Further, it discusses the pedagogical methods introduced by teachers and trainers to enhance the effectiveness of students' learning and their development as future entrepreneurs. It explains the various initiatives generally undertaken to broaden the scope of entrepreneurship education by extending it beyond regular students and offering it to other groups such as professionals, technicians, artisans, war veterans, and the unemployed. The book is a valuable resource for researchers and academics working in the field of entrepreneurship education as well as for trainers, consultants, mentors and policy makers. Springer Nature Singapore Pte Ltd. 2017. All rights are reserved. -
Entrepreneurship education: Innovations and best practices
Entrepreneurship education has become a priority for policy-makers especially in developing countries. Such interventions in the education system are expected to create a culture of entrepreneurship in the society and thereby bring economic benefits through the enterprising behaviour of individuals resulting in better performance of existing organizations as well as creation of new ventures. While the process appears to be simple and straightforward, the experiences have often belied the expectations. The fact that it is rather difficult to assess the long-term impact of entrepreneurship education adds to the confusion and ambiguities. Educators therefore have been tinkering with various aspects of entrepreneurship education and training in the hope of arriving at the best design. Obviously, this has led to many innovations in the curriculum, pedagogy, target groups and institutions involved in entrepreneurship education. The present paper attempts to document these innovations and best practices under a 'WHAT-HOW-WHO-WHERE' framework to capture the four domains of activities involved. Based on a comprehensive review of the literature, we have developed a fairly comprehensive picture of what is happening in the field and proposed a theoretical model highlighting the dual role of entrepreneurship education, namely developing enterprising individuals in the society and providing knowledge and skills required for enterprise creation. Springer Nature Singapore Pte Ltd. 2017. All rights are reserved. -
Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image
Segmentation of abnormal masses in kidney images is a tough task. One of the main challenges is the presence of speckle noise, which will restrain the valuable information for the medical practitioners. Hence, the detection and segmentation of the affected regions vary in accuracies. The proposed model includes pre-processing and segmentation of the diseased region. The pre-processing consists of Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) to improve the clarity of the images. Further, segmentation has been done based on the entropy of the image and gamma correction has been done to improve the overall brightness of the images. An optimal global threshold value is selected to extract the region of interest and measures the area. The model is analyzed with statistical parameters like Jaccard index and Dice coefficient and compared with the ground truth images. To check the accuracy of the segmentation, relative error is calculated. This framework can be used by radiologists in diagnosing kidney patients. 2022, International Journal of Computing. All Rights Reserved.