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Blockchain for seamless and transparent smart hospitality operations
This chapter explores how blockchain technology is transforming the hospitality industry by enhancing data transparency, operational efficiency, and security. It highlights blockchain's role in securing payments, bookings, supply chains, and guest authorization processes. Through smart contracts and tamper-proof records, blockchain combats fraud and ensures compliance with regulatory standards. The chapter examines real-world use cases such as loyalty platforms that boost customer retention and brand trust. Despite challenges in scalability, operations, and regulations, leading hospitality firms have achieved improved guest interactions through blockchain integration. This chapter provides insights into safely adopting blockchain for hotel management, emphasizing its potential to build trust, streamline operations, and align with smart infrastructure systems. 2026, IGI Global Scientific Publishing. -
An outlook in blockchain technology- Architecture, applications and challenges
Blockchain is mechanism which stores and exchange data in a peer-peer network serving as an immutable ledger allowing transactions to take place in decentralized method which neglects the role of intermediaries. The technology reduces greater complexity by combining three key features; security, decentralization and transparency. This paper is an attempt explaining the concepts, structure, applications and challenges the technology has. The paper introduces blockchain taxonomy, reviews applications and discussed technical challenges and way of handling these challenges. Blockchain technology is springing up with promising applications in various fields and the authors have explored about three emerging field of blockchain say; Education, Government and Healthcare. Finally the paper concludes by stating other emerging fields of applications where further research can be explored. International Research Publication House. -
Gulaab gang: Is it about the battle of sexes or women empowerment or cliches? /
Indian Research Journal, Vol.1, Issue 7, pp.40-45, ISSN No: 2347-7695. -
Dynamical analysis of a model of social behavior: Criminal vs non-criminal population
In this paper, we construct a model motivated by the well known predator-prey model to study the interaction between criminal population and non-criminal population. Our aim is to study various possibilities of interactions between them. First we model it using simple predator-prey model, then we modify it by considering the logistic growth of non-criminal population. We clearly deduce that the model with logistic growth is better than classical one. More precisely, the role of carrying capacity on the dynamics of criminal minded population is discussed. Further, we incorporate law enforcement term in the model and study its effect. The result obtained suggest that by incorporating enforcement law, the criminal population reduces from the very beginning, which resembles with real life situation. Our result indicates that the criminal minded population exist as long as coefficient of enforcement lc does not cross a threshold value and after this value the criminal minded population extinct. In addition, we also discuss the occurrence of saddle-node bifurcation in case of model system with law enforcement. Numerical examples and simulations are presented to illustrate the obtained results. 2017 Elsevier Ltd -
An efficient inclusion complex based fluorescent sensor for mercury (II) and its application in live-cell imaging /
Journal of Fluorescence, Vol.32, pp.1109–1124, ISSN No: 1573-4994.
The formation of an inclusion complex between hydroxypropyl-β-cyclodextrin (H-CD) and 4-acetylphenyl-4-(((6-chlorobenzo[d]thiazol-2-yl)-imino)-methyl)-benzoate (L) was investigated by FT-IR, 1H-NMR, X-ray diffraction (XRD), FT-Raman, scanning electron microscope (SEM) techniques in the solid-state, absorption and emission spectroscopy in the liquid state and the virtual state as molecular docking technique. The binding properties of the inclusion complex (H-CD: L) with cations in deionized water was observed via absorbance and photoluminescence (PL) emission spectroscopy. -
Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN
Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. -
A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning
Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. The Author(s) 2024. -
Mixed radiated magneto Casson fluid flow with Arrhenius activation energy and Newtonian heating effects: Flow and sensitivity analysis
The characteristics of Stefan blowing effects in a magneto-hydrodynamic flow of a Casson fluid past a stretching sheet are investigated. The effects of radiation, heat source/sink, Newtonian heating, Arrhenius activation energy and binary chemical reaction are considered for heat and mass transfer analysis. The homotopy analysis method (HAM) was utilised to solve the transformed non-dimensionalized equations analytically. The impact of various physical parameters affecting the flow are investigated. Further, the relationship of various parameters on the skin friction and rate of heat and mass transfer was explored using correlation and probable error. A sensitivity analysis was carried out based on the Response Surface Methodology to analyse the effect of Stefan blowing parameter, magnetic parameter and stretching/shrinking parameter on the reduced Nusselt number and reduced Sherwood number. A constant positive sensitivity for the reduced Nusselt number towards the Stefan blowing parameter for all levels of magnetic parameter and stretching/shrinking parameter was found. Further, the reduced Sherwood number indicated a negative sensitivity towards the Stefan blowing parameter. 2020 Faculty of Engineering, Alexandria University -
Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications
This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for about 58%. 2022 SulaimaLebbe Abdul Haleem et al. -
Stacked LSTM and Kernel-PCA-based Ensemble Learning for Cardiac Arrhythmia Classification
Cardiovascular diseases (CVD) are the most prevalent causes of death and disability worldwide. Cardiac arrhythmia is one of the chronic cardiovascular diseases that create panic in human life. Early diagnosis aids physicians in securing life. ECG is a non-stationary physiological signal representing the heart's electrical activity. Automated tools to detect arrhythmia from ECG signals are possible with Machine Learning (ML). The ensemble learning technique combines the power of two or more classifiers to solve a computational intelligence problem. It enhances the performance of the models by fusing two or more models, which extremely increases its strength. The proposed ensemble Machine learning amalgamates the potency of Long Short-Term Memory (LSTM) and ensemble learning, opening up a new direction for research. In this research work, two novel ensemble methods of Extreme Gradient Boosting-LSTM (EXGB-LSTM) are developed, which use LSTM as a base learner and are transformed into an ensemble learner by coalescing with Extreme Gradient Boosting. Kernel Principal Component Analysis (K-PCA) is a significant non-linear dimensionality reduction technique. It can manage highdimensional datasets with various features by lowering the dimensionality of the data while retaining the most crucial details. It has been applied as a preprocessing step for feature reduction in the dataset, and the performance of EXGB-LSTM is tested with and without K-PCA. Experimental results showed that the first method, fusion of EXG-LSTM, has reached an accuracy of 92.1%, Precision of 90.6%, F1-score of 94%, and Recall of 92.7%. The second proposed method, KPCA with EXGB-LSTM, attained the highest accuracy of 94.3%, with a precision of 92%, F1-score of 98%, and Recall of 94.9% for multi-class cardiac arrhythmia classification. (2023), (Science and Information Organization). All Rights Reserved. -
Decoding the Surge in Rural Employment Understanding the Post-pandemic Shift in Rural India
The increase in the rural workforce during and after the lockdown was primarily driven by women entering self-employment in agriculture, where earnings remain significantly low. The movement from casual work to self-employment in the agricultural sector also underlines a larger shift in dynamics in rural areas, arguably exacerbated by the pandemic. The non-manufacturing sector, most importantly the construction sector, absorbed men in the post-pandemic situation, but the same shift is not observed among women. 2026, Economic and Political Weekly. All rights reserved. -
Effect of calcium sulfoaluminate additive on linear deformation at different humidity and strength of cement mortars
The effect of calcium Sulfoaluminate additives (CSA) on the compression and bending strength of mortar, as well as linear deformation of prism samples at different environmental humidity was studied. Test results indicate that bending strength of mortars with CSA and the referent at the age of 28 days are practically equal. Compressive strength of mortars with CSA reduced by 20... 23% for all dosages of CSA. Relative linear deformations depend on the humidity of the environment. At a humidity of 100%, the relative linear deformations are positive and the expansion increases with increasing dosage of the expanding additive. When hardening in dry air at a humidity of 55%, the greatest shrinkage deformations were observed for mortars with CSA. We can conclude that the expanding effect of CSA is fully manifested at high humidity, i.e. under construction conditions, this means very high-quality moisture care for concrete structures. The Authors 2020. -
From Producer to Consumer: AI-Blockchain Integration for Sustainable Supply Chain Tracking and Optimization
The blockchain is transforming the way supply chains operate. It is a decentralized peer-to-peer system that ensures safe and transparent data interchange. Because there is no central authority, blockchain technology can't be hacked; the data are dispersed throughout numerous nodes. Therefore, more openness, safety, and resistance to tampering are assured as opposed to conventional centralized database systems that depend on a single authority to manage all the data. This would ensure the immutability of the transaction log, which accurately traces the products from origin to destination. The supply chain refers to the transportation and distribution of goods when transactions are verified in real time and integrated into a secure, cryptographic ledger. This decentralized framework provides better tracking of various manufacturers, reduced chances of fraud, and greater trust among the participants. The early understanding based on tracking will definitely helps the stakeholders to improve the blockchain activities, reliability, it will minimize risks, and enhances efficiency. This will parallelly help those who are involved within supply chain management giving high accountability and seamlessness in the movement of details. 2025 IEEE. -
Artificial Intelligence-Monitored Procedure for Personal Ethical Standard Development Framework in the E-Learning Environment
The changes in the lifestyle of human beings due to the pandemic COVID-19 have affected all walks of human life. As a pillar of human development, the arena of education has a vital role to play in this changing world. The humongous and disruptive technologies that had made inroads into the educational scene as E-learning paved the way for ethical concerns in an unimaginable manner. Artificial intelligence is prudently incorporated for developing an ethical lifestyle for students all over the world. The Personal Ethical Standard Framework would work as a vaccine for the pandemic of the cancerous growth of the unethical habits of learners. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A study exploring the effect of subliminally priming known human faces vs. unknown human faces on product selections by consumers: Unseen motivators
Unconscious thoughts more than often are seen to precede conscious contemplations of the surroundings. The Present chapter attempts to explore how subliminal priming of known and unknown human faces could impact product selection and decision-making time of consumers. 2 (Known face X Unknown face) X 2 (Product selection X Decision-making time) within-subject design was used for the study. A stimulus-priming experiment designed in E-prime software was used to subliminally expose the participants to both known and unknown human faces They were then asked to select a product that they were willing to buy from an option of four products, of which one of the products was primed along with Human face (Known Vs Unknown). The product selection rates as well as the time taken to select the product were recorded. A total of 100 Participants falling in the age category of young adults (18-39) took part in the study. The chapter discusses the results and dives deeper into the implications that they hold in the world of marketing. 2024, IGI Global. All rights reserved. -
Unseen motivators: A study exploring the effect of subliminally priming known human faces vs unknown human faces on consumers product selection decisions
The human mind is constantly being influenced by a vast number of external stimuli that are perceived consciously as well as unconsciously. The chapter attempts to explore how unconscious (subliminal) priming of known and unknown human faces could impact product selection and decision-making time of consumers. 2 (Known face X Unknown face) X 2 (Product selection X Decision-making time) within-subject design was used for the study. A pilot study was conducted to estimate the subliminal time threshold of the population. It was found to be 17ms. A stimulus-priming experiment designed in Opensesame software was used to subliminally expose the participants to both known and unknown human faces. They were then asked to select a product that they were willing to buy from an option of four products, of which one of the products was primed along with human face (known vs. unknown). The product selection rates as well as the time taken to select the product were recorded. A total of 100 participants falling in the age category of young adults (18-39) took part in the study. 2024, IGI Global. All rights reserved. -
Exploring Factors Influencing Digital Inclusion Within Modern Pedagogical Practices Prevalent Amongst Schoolteachers: Smart Students, Smarter Teachers?
Teachers serve as torchbearers to the vast expanse of knowledge, leaving a lasting impact on the minds of their students through their teaching styles and pedagogical practices. With the advancement of technology, the integration of digital technologies has increasingly been observed in pedagogical approaches. The current study seeks to explore various subjective factors related to teachers, such as their teaching styles, age, classes taught, educational qualifications, and years of experience, in relation to their individual digital competencies and the extent of digital inclusivity in their pedagogies. Three psychometric scales were employed for data collection (TSI-Q, DiPeCoS, and TDiCoS). Data were gathered from 215 school teachers in the United Arab Emirates (UAE) and subsequently analysed. The results revealed a significant influence of teaching styles (p = 0.045), classes taught (p = 0.001), age (p = 0.006), and years of teaching experience (p = 0.016) on the digital inclusivity of the teachers' pedagogical practices. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Decolonizing the Home at Home in the Pandemic: Articulating Women's Experience
Feminism bears the promise of liberation of and equality for women. Reading and teaching feminist texts, within the academia and in activist spaces, has provided the opportunity to explore what it means to become and be a woman. This article explores the experience of teaching a course on women's writing at the undergraduate level during the COVID-19 pandemic. Normally, a course on feminist writings is an occasion for self-reflection, thereby providing an opportunity to establish a dialogue between the domestic and the public. Such dialogues took place in secure institutional spaces such as classrooms or conference halls, without the intrusion of the domestic. However, as the teacher-student interaction shifted to an online mode during the pandemic, all the participants in this dialogue, including the instructor and the students, found themselves in domestic spaces, with family members listening. The article chronicles the anxieties of a woman instructor, as she teaches feminist texts from home to learners who are sitting behind computer screen in their homes and the possible impact of feminist ideas on the domestic spaces of all participants. 2022 The Author(s). Published by Oxford University Press on behalf of the English Association. All rights reserved. -
God has signed: Nature, divinity and mysticism in the poetry of Kuvempu
Kuvempu wrote a large number of poems on the mysteries of nature. Kuvempu hails from the heart of Western Ghats and he spent his childhood and youth exploring the forests around his house. Untrammelled nature was both mysterious and beautiful; hence nature turned out to be a primary inspiration to write poetry. Kuvempu looks outward, seeking to comprehend the oneness of all in nature through his senses. But he is also struck by the inability to comprehend and explain nature through senses. Often he expresses his awe at natural sights such as dawn (which appears to him as a God's signature) or the greenery of Western Ghats (which seems to have painted everything in nature in green, including poet's soul and the blood in the stomach). This leads Kuvempu to resort to mysticism in order to relate, comprehend and sing about nature. He sees in nature the divine presence. The paper will analyze poems such as Devaru Rujumadidanu, Ba Phalguna Ravidarshanake, and Prakriti Upasane, and explore the poetic perception of nature as divine through mysticism. 2014 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore).