Browse Items (16481 total)
Sort by:
-
Analyzing the Impact of Blockchain Technology on Financial Transactions and Accounting Practices in Global Trade
Financial transactions and accounting practises in global trade are transformed using blockchain technology with enhanced security efficiency and transparency. The study examines the role of Blockchain in real time financial reports smart contracts and cross border transactions by highlighting the potentiality to ensure regulatory compliance streamlined reconciliation process and fraudulence reduction. Decentralisation of financial data using blockchain enable record keeping a stamper proof and also minimises the transaction cost and the intermediaries and to accelerate the settlement times. The study explores the challenges such as integration regulatory uncertainties and scalability in the existing financial system. The study involves trade finance platforms and multinational corporations in reshaping the financial management and accounting standards using blockchain adoption in the global trade. The study employs mixed method approach for quantitative and qualitative analysis based on the ability of the blockchain to enhance the financial transactions in global trade. 2025 IEEE. -
Climate Change Impact on Water Resources, Food Production and Agricultural Practices
The greatest threat to human health that exists today is climate change. Ecosystems, societies and biodiversity are seriously at risk from the long term effects due to change in climate, primarily brought on by human activities. Rising temperatures increase evaporation, which causes drought and decreases water availability for ecosystems, drinking water supplies and agriculture. Changed precipitation patterns exacerbate floods, storms and sea levels, contaminating the water supply and harming infrastructure. The effects of rapidly changing climate on water resources must be minimised through sustainable water management techniques, conservation initiatives and International initiatives. The effects of climate change on the long run have been the focus of research because stable weather significantly influences agricultural productivity. Due to agricultures reliance on temperature and rainfall, climate change threatens world food security. Rising temperature results in lower productivity and also promotes the growth of weeds and pests, changes precipitation patterns, which will result in more crop failures and production declines. This work summarises the outcome of climate change on crop and livestock yields, water resources and the economy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Influence of Material Composition and Printing Parameters on Impact Strength and Hardness Properties of SLA-Fabricated BN/Resin Composites
The impact strength and hardness characteristics of boron nitride (BN) reinforced photosensitive resin composites made by stereolithography (SLA) 3D printing are examined in this work in relation to the effects of material composition and printing settings. Taking into account the process factors of material composition, lift speed, build angle, and post-curing time, a Taguchi L16 orthogonal array was utilized to optimize the design parameters. Analysis of variance (ANOVA) and the signal-to-noise (S/N) ratio were used to examine the experimental data. Material composition of 1 wt% BN, build angle of 0, post-curing time of 60 min, and lift speed of 30 mm/min were the ideal process parameters for high impact strength, according to the S/N ratio analysis. For high hardness, the ideal parameters were the material composition of 1 wt% BN, a build angle of 90, a post-curing time of 90 min, and a lift speed of 45 mm/min. According to ANOVA results, the build angle had the biggest impact on hardness (56.74%), whereas post-curing time had the biggest impact on impact strength (49.66%). The study also indicates that all parameters should be tuned simultaneously for their combined influence on the mechanical characteristics, according to interaction graphs. 2025 John Wiley & Sons Ltd. -
Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications
This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premiseenabling machines to learn optimal actions within complex environments through trial and errorhas broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you're an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency. 2024 Scrivener Publishing LLC. -
Tracing the Legal Invisibility and Challenges of Same-Sex Couples in India
Although the Supreme Court's decision in Supriyo v. Union of India (2023) concerning marriage equality rights of same-sex couples has gained public attention, couples continue to face legal invisibility as the Court left the question of legal recognition to parliament. This study examines the legal challenges faced by same-sex couples in India in accessing marriage, family formation, healthcare and housing rights and seeks to understand their lived experiences in the context of limited legal recognition. It highlights how the lack of recognition deepens social vulnerability while also exposing couples to discrimination and, in some cases, abuse. Semi-structured, in-depth interviews were conducted with 25 couples recruited via snowball sampling, and data were thematically analyzed. The findings reveal that the participants continue to face legal blockades in exercising their rights, underscoring the need for urgent legislative reform. 2026 Policy Studies Organization. -
IoT Framework, Architecture Services, Platforms, and Reference Models
Internet of things (IoT) is spawning a twirl in the world of connected devices by aiding the devices to connect, compute, and coordinate with each other. While the concept of IoT is still embryonic, its outcomes are trailblazing. IoT acts as a facilitator in creating a smart world by connecting devices through sensors and actuators to the Internet. The acceptance of IoT in various sectors indicates that the partakers in an IoT ecology are diverse. This demands common functionalities, interoperability standards, and network protocols across sectors. But there exists an extremity of incongruency in devices, capabilities, and network protocols, and therefore it is imperative to have a complete reference architecture model that necessitates the existing diversities and defines a new monody for the IoT environment. The lack of standard and uniform architectural knowledge, frameworks, and platforms is presently resisting the researchers to reap the benefits that the Internet of things (IoT) offers. This chapter summarizes various Internet of things frameworks, architectures, platforms, and reference models and thereby paves way for businesses to build IoT on it. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Cloud Intrusion Detection Using Hybrid Convolutional Neural Networks
Instead of storing data on a hard drive, cloud computing is seen as the best option. The Internet is used to deliver three different kinds of computing services to users all over the world. One advantage that cloud computing provides to its customers is greater access to resources and higher performance while at the same time increasing the risk of an attack. Intrusion detection systems that can handle a large volume of data packets, analyse them, and generate reports based on knowledge and behaviour analysis were developed as part of this research. As an added layer of protection, the Convolution Neural Network Algorithm is used to encrypt data during end-to-end transmission and to store it in the cloud. Intrusion detection increases the safety of data in the cloud. In this paper demonstrates the data is encrypted and decrypted using a model of an algorithm and explains how it is protected from attackers. It's important to take into account the amount of time and memory required to encrypt and decrypt large text files when evaluating the proposed system's performance. The security of the cloud has also been examined and compared to other existing encoding methods. 2024, Iquz Galaxy Publisher. All rights reserved. -
AI-Driven Health Coach for Diabetes Management
Artificial intelligence (AI) is transforming diabetes care through innovative approaches that enhance monitoring, prediction, and treatment. AI-powered health coaches exemplify this progress by automating various aspects of patient care, such as creating personalized dietary plans and managing medication schedules, thereby optimizing resource utilization with minimal human intervention. In India, where diabetes affects over 77 million people and significantly elevates the risk of complications like heart disease and stroke, AI-driven tools offer immense potential. Food recognition and nutritional apps powered by AI can revolutionize diabetes management by tracking dietary intake and providing tailored recommendations. However, widespread adoption faces barriers, including challenges related to localization, cultural relevance, and integration with healthcare systems. This chapter examines the role of AI in diabetes management, evaluating the benefits and limitations of current applications. It also proposes a framework for an AI-driven health coach tailored to the Indian context. The proposed solution aims to bridge existing gaps by delivering accurate, culturally sensitive, and integrated diabetes management tools, ultimately improving long-term health outcomes for Indian patients. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES
Aim/Purpose In this research work, we have developed a predictive model that focuses on utilizing knowledge from the related domains. Background A serious public health issue, especially in tropical and subtropical regions, is dengue fever, a viral infection passed by mosquitoes. Accurate early prediction of disease outcomes is essential for both efficient patient management and ef-fective use of resources. More complex methods are required since conven-tional prediction models could be faulty with limited labeled data and complex feature interactions. Methodology We propose a new strategy integrating deep attention mechanisms with trans-fer learning to enhance prediction modeling of dengue disease outcomes. First pre-trained on a large, linked dataset of common viral illnesses, a deep neural network enables the model to learn generic properties. We then iteratively im-prove our pre-trained model using a specific dengue dataset. Incorporating a deep attention mechanism allows for the focus on the most relevant features, improving interpretability and accuracy. Contribution Among logistic regression, random forests, and basic deep learning methods, current models reveal poor accuracy and dependability in forecasting dengue disease outcomes. These models sometimes fail to sufficiently depict the com-plicated interactions among clinical variables, especially under conditions with limited data. Findings The proposed method outperforms more traditional models pretty strongly. Our model acquired in the training phase an accuracy of 0.92, precision of 0.91, recall of 0.90, and F1-score of 0.90. It maintained high performance on testing with an accuracy of 0.91, precision of 0.90, recall of 0.89, and an F1-score of 0.89. Similar patterns were indicated by an accuracy of 0.90, precision of 0.89, recall of 0.88, and an F1-score of 0.88 validation results. The model also demonstrated a lowered loss (0.21, 0.23, 0.24 in training, testing, and vali-dation, respectively), higher true positive rates (0.90, 0.89, 0.88), and lower false positive rates (0.10, 0.11, 0.12). Deep attention methods and transfer learning offer a robust and effective strategy for predictive modeling of dengue disease outcomes, therefore considerably boosting accuracy and dependability. This approach offers considerable possibilities for dengue-endemic patient manage-ment and resource allocation. Recommendations for Researchers Investigations should prioritize the validation of the algorithm in various healthcare environments to assess its efficacy in clinical application. Future Research In future research, this work can be enhanced using several deep learning algo-rithms to achieve better accuracy and performance. This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes. -
Does environmental policy stringency improve nature's health in BRICS economies? Implications for sustainable development
In our groundbreaking exploration, we meticulously delve into the relationship between environmental policy stringency, international trade dynamics, and financial openness within the BRICS group (Brazil, Russia, India, China, and South Africa) spanning from 1996 to 2021. With a focus on critical variables such as economic growth and technological innovation, our empirical findings challenge conventional wisdom. Surprisingly, we found that those stringent environmental policies, when standing alone, do not invariably lead to reduce CO2 emissions. Equally interesting is our startling discovery that the anticipated moderating influence of environmental policy stringency, catalyzed by trade and foreign direct investment, on the well-being of our environment does not materialize; contrarily, both trade and foreign direct investment moderating channels exhibit unanticipated positive correlations with CO2 emissions. These revelations provoke us with the presence of a "pollution haven" phenomenon within the BRICS economies. Furthermore, our investigation reveals that, when examined individually, trade and foreign direct investment also appear to contribute to elevated emission levels. These findings provide a resolute solution to our research quandary, underlining the indispensable requirement for cutting-edge and robust environmental policies. These policies must possess the prowess to effectively counteract the adverse environmental consequences stemming from the amalgamation of global trade and financial integration. In doing so, they shall propel BRICS nations toward a future firmly grounded in principles of sustainability and ecological integrity. 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
A reliable inter-domain routing framework for autonomous systems using hybrid Blockchain
Inter-domain networks face several routing challenges, such as security, scalability, and reliability concerns in existing BGP-based systems. These challenges are exacerbated by the increasing number of interconnected networks and the lack of a standardized approach for routing data between them. Hybrid Blockchain-based framework has proposed for inter-domain routing in autonomous systems in this research. The framework combines the use of traditional routing protocols with the distributed ledger technology of Blockchain. It leverages the salient features of both to create a more secure and efficient routing framework. The Blockchain component provides a decentralized and tamper-proof ledger for storing routing information, while the traditional routing protocols handle the actual exchange of data between autonomous systems. The framework is designed to enhance the security of inter-domain routing by incorporating the use of digital signatures and information sharing among participating autonomous systems. Each participating system maintains a copy of the distributed ledger and can verify the authenticity of routing information using digital signatures. It ensures that only legitimate and authorized data is transmitted between autonomous systems, mitigating the risk of malicious attacks or illegitimate routing. The proposed framework obtained 87.73 % Route calculation Speed, 90.41 % Route filtering, 93.77 % Fault tolerance, 94.10 % Load balancing, 95.54 % Hop count, 95.13 % bandwidth consumption, 93.94 % Security Management and 96.29 % Convergence time. The framework employs a consensus mechanism for updating and validating the routing information, ensuring consistency and accuracy in the routing decisions. It also reduces the reliance on a single central authority and distributes the decision-making process among participating systems. 2024 Elsevier Ltd -
A reliable inter-domain routing framework for autonomous systems using hybrid Blockchain
Inter-domain networks face several routing challenges, such as security, scalability, and reliability concerns in existing BGP-based systems. These challenges are exacerbated by the increasing number of interconnected networks and the lack of a standardized approach for routing data between them. Hybrid Blockchain-based framework has proposed for inter-domain routing in autonomous systems in this research. The framework combines the use of traditional routing protocols with the distributed ledger technology of Blockchain. It leverages the salient features of both to create a more secure and efficient routing framework. The Blockchain component provides a decentralized and tamper-proof ledger for storing routing information, while the traditional routing protocols handle the actual exchange of data between autonomous systems. The framework is designed to enhance the security of inter-domain routing by incorporating the use of digital signatures and information sharing among participating autonomous systems. Each participating system maintains a copy of the distributed ledger and can verify the authenticity of routing information using digital signatures. It ensures that only legitimate and authorized data is transmitted between autonomous systems, mitigating the risk of malicious attacks or illegitimate routing. The proposed framework obtained 87.73 % Route calculation Speed, 90.41 % Route filtering, 93.77 % Fault tolerance, 94.10 % Load balancing, 95.54 % Hop count, 95.13 % bandwidth consumption, 93.94 % Security Management and 96.29 % Convergence time. The framework employs a consensus mechanism for updating and validating the routing information, ensuring consistency and accuracy in the routing decisions. It also reduces the reliance on a single central authority and distributes the decision-making process among participating systems. 2024 Elsevier Ltd -
Sonochemical assisted impregnation of Bi2WO6 on TiO2 nanorod to form Z-scheme heterojunction for enhanced photocatalytic H2 production
In this work, Bi2WO6/TiO2 nanorod heterojunction was prepared by sonochemical assisted impregnation method. After loading 2 wt% Bi2WO6 on TiO2 nanorods, the photocatalytic hydrogen production rate of 2026 mol/h/g was achieved. Compared to commercial P25 and TiO2 nanorods, ?13 and ?3 folds enhanced activity was observed. The excellent photocatalytic performance of Bi2WO6/TiO2 nanorod photocatalyst was mainly attributed to i) reduction of bandgap due to heterojunction formation, ii) quick transport of photogenerated charge carriers, and iii) efficient charge carrier separation supported by UV-DRS, photocurrent measurement, Impedance study, and photoluminescence spectra analysis. The Z-scheme band alignment for Bi2WO6/TiO2 nanorod heterojunction was proposed based on the Mott-Schottky measurement. This result demonstrated the effective utilization of Z-scheme heterojunction of Bi2WO6/TiO2 for photocatalytic reduction application. 2021 The Society of Powder Technology Japan -
Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks
Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms. 2023 IEEE. -
Selection of cobot for human-robot collaboration for robotic assembly task with Best Worst MCDM techniques
Since the first industrial robot was produced at the beginning of the 1960s, robotic technology has completely changed the sector. Industrial robots are made for various tasks, including welding, painting, assembling, disassembling, picking and placing printed circuit boards, palletizing, packing and labeling, and product testing. Finding flexible solutions that allow production lines to be swiftly re-planned, adjusted, and structured for new or significantly modified product development remains a significant unresolved problem. Today's Industrial robots are still mostly pre-programmed to do certain jobs; they cannot recognize mistakes in their work or communicate well with both a complicated environment and a human worker. Full robot autonomy, including organic interaction, learning from and with humans, and safe and adaptable performance for difficult tasks in unstructured contexts, will remain a pipe dream for the foreseeable future. Humans and robots will work together in collaborative settings such as homes, offices, and factory setups to execute various object manipulation activities. So, it is necessary to study the collaborative robots (cobots) that will play a key role in human-robot collaborations. Multiple competing variables must be considered in a thorough selection process to assess how well industrial cobots will work on an industrial working floor. To select a collaborative robot for the human-robot collaborative application, a straightforward multi-criteria decision-making (MCDM) methodology is based on the best-worst method (BWM). The ranking derived using the BWM method is displayed. The outcomes demonstrated the value of MCDM techniques for cobot selection. 2023 IEEE. -
Flow and heat transport of nanomaterial with quadratic radiative heat flux and aggregation kinematics of nanoparticles
A numerical study of flow and heat transport of nanoliquid with aggregation kinematics of nanoparticles is carried out using the modified Buongiorno model (MBM). The MBM model is composed of random motion nanoparticles, heat diffusion of nanoparticles, and effective properties of nanoliquids. The effects of quadratic variation of density-temperature (quadratic convection), and the quadratic Rosseland thermal radiation are also studied. Inclined magnetism is also taken into account. The aggregation kinematics of nanoparticles is simulated using the modified Krieger-Dougherty model for dynamic viscosity and the modified Maxwell model for thermal conductivity. The main system of nonlinear partial differential equations is solved using the similarity technique and the finite difference method-based algorithm (FDM). The consequence of several key parameters on velocity, nanoparticle volume fraction, wall heat flux, and temperature are found in two cases, namely weak convective heating and strong convective heating. The study reveals that the suspension of the nanoparticles increases the thermal conductivity and, thus, improves the temperature and reduces the heat flux at the plate. The structures of the thermal and velocity surface layer are higher in the case of strong convective heating, while in the case of weak convective heating, the nanoparticle volume fraction layer is thicker. 2021 Elsevier Ltd -
Magnetohydrodynamic flow of Carreau liquid over a stretchable sheet with a variable thickness: The biomedical applications
Purpose: The magnetohydrodynamic (MHD) flow problems are important in the field of biomedical applications such as magnetic resonance imaging, inductive heat treatment of tumours, MHD-derived biomedical sensors, micropumps for drug delivery, MHD micromixers, magnetorelaxometry and actuators. Therefore, there is the impact of the magnetic field on the transport of non-Newtonian Carreau fluid in the presence of binary chemical reaction and activation energy over an extendable surface having a variable thickness. The significance of irregular heat source/sink and cross-diffusion effects is also explored. Design/methodology/approach: The leading governing equations are constructed by retaining the effects of binary chemical reaction and activation energy. Suitable similarity transformations are used to transform the governing partial differential equations into ordinary differential equations. Subsequent nonlinear two-point boundary value problem is treated numerically by using the shooting method based on RungeKuttaFehlberg. Graphical results are presented to analyze the behaviour of effective parameters involved in the problem. The numerical values of the mass transfer rate (Sherwood number) and heat transfer rate (Nusselt number) are also calculated. Furthermore, the slope of the linear regression line through the data points is determined in order to quantify the outcome. Findings: It is established that the external magnetic field restricts the flow strongly and serves as a potential control mechanism. It can be concluded that an applied magnetic field will play a major role in applications like micropumps, actuators and biomedical sensors. The heat transfer rate is enhanced due to Arrhenius activation energy mechanism. The boundary layer thickness is suppressed by strengthening the thickness of the sheet, resulting in higher values of Nusselt and Sherwood numbers. Originality/value: The effects of magnetic field, binary chemical reaction and activation energy on heat and mass transfer of non-Newtonian Carreau liquid over an extendable surface with variable thickness are investigated for the first time. 2020, Emerald Publishing Limited. -
Computational modeling of heat transfer in magneto-non-Newtonian material in a circular tube with viscous and Joule heating
Numerous industrial and engineering systems, like, heat exchangers, chemical action reactors, geothermic systems, geological setups, and many others, involve convective heat transfer through a porous medium. The diffusion rate, drag force, and mechanical phenomenon are dealt with in the DarcyForchheimer model, and hence this model is vital to study the fluid flow and heat transport analysis. Therefore, numerical simulation of the DarcyForchheimer dynamics of a Casson material in a circular tube subjected to the energy losses due to the viscous heating and Joule dissipation mechanisms is performed. The novelty of the present investigation is to scrutinize the convective heat transport characteristics in a circular tube saturated with DarcyForchheimer porous matrix by utilizing the non-Newtonian Casson fluid. The flow occurs due to the elongation of the surface of a tube with a uniform heat-based source/sink. The similarity solution of the nonlinear problem was obtained using dimensionless similarity variables. The effects of operating parameters related to the flow phenomena are analyzed. Further, the friction factor and Nusselt number are also analyzed in detail. The present flow model ensures no flow reversal and acts as a coolant of the heated cylindrical surface; the existence of the magnetic field, as well as an inertial coefficient,acts as the momentum-breaking forces, whereas Casson fluidity buildsit. The Joule heating phenomenon enhances the magnitude of temperature. The thermal field of the Casson fluid is higher at the surface of the circular pipe due to convective thermal conditions. 2021 Wiley Periodicals LLC. -
Effectiveness of exponential heat source, nanoparticle shape factor and Hall current on mixed convective flow of nanoliquids subject to rotating frame
Purpose: The study of novel exponential heat source (EHS) phenomena across a flowing fluid with the suspension of nanoparticles over a rotating plate in the presence of Hall current and chemical reaction has been an open question. Therefore, the purpose of this paper is to investigate the impact of EHS in the transport of nanofluid under the influence of strong magnetic dipole (Hall effect), chemical reaction and temperature-dependent heat source (THS) effects. The Khanafer-Vafai-Lightstone model is used for nanofluid and the thermophysical properties of nanofluid are calculated from mixture theory and phenomenological laws. The simulation of the flow is also carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e. sphere, hexahedron, tetrahedron, column and lamina). Design/methodology/approach: Using Laplace transform technique, exact solutions are presented for the governing nonlinear equations. Graphical illustrations are pointed out to represent the impact of involved parameters in a comprehensive way. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for 20 different nanofluids are presented. Findings: It is established that the nanofluid enhances the heat transfer rate of the working fluids; the nanoparticles also cause an increase of viscous. The impact of EHS advances the heat transfer characteristics significantly than usual thermal-based heat source (THS). Originality/value: The effectiveness of EHS phenomena in the dynamics of nanofluid over a rotating plate with Hall current, chemical reaction and THS effects is first time investigated. 2019, Emerald Publishing Limited.