bluuebunny/crossref_metadata_2025 huggingface dataset:
The Modern Rise of Bioinformatics Through Ecoinformatics
Authors: Cánovas Fernando García
Published: November 2024

Bioinformatics focuses in the use of computer technology to process biological information, particularly genetic data. However, bioinformatics has revolutionized the way scientists analyze such data by addressing the challenges of biodiversity conservation and ecological research. As a result, ecoinformatics emerges from the intersection between ecological research and big data science, enabling more effective analysis, monitoring, and management of biodiversity. Adaptive management and dynamic conservation planning is now relying on the use of real-time data, citizen science and public engagement in conservation biology. The future of biodiversity conservation through ecoinformatics holds enormous potential as advanced computational tools, big data, and innovative technologies continue to evolve, providing more integrated data-management solutions and effective analysis of natural phenomena.
Exploring the Opportunities and Confronting the Challenges Within Business Incubation
Authors: Inamdar Saba , Afroze Sameera
Published: November 2024

Business incubation is a public or private development process that nurtures firms from idea creation to start-ups, providing complete assistance to help them grow and succeed. An incubator offers resources including, physical space, capital guidance, shared services, and networking opportunities. Incubators provide mentoring, business aid, and monitoring services. New enterprises are facilitated by raising awareness of risks and reducing the cost of failure. With over 10,000 incubators globally, the business incubation process has grown more important for start-ups, and extensive research has been conducted on the process, encompassing studies on its interventions and consequences. The current study advances understanding of the processes influencing particular performance and results and the incubation process. It also examines the many kinds of business incubators, benefits, and drawbacks. It clarifies the goals of future research and how policymakers and incubation managers might design interventions according to the particular incubator type and the process setting.
Economic and Social Impact of Business Incubation in UAE
Authors: Iyer Shankar Subramanian
Published: November 2024

Business incubation programs have emerged as pivotal mechanisms for fostering entrepreneurship and innovation, especially within the rapidly evolving economic landscape of the UAE. This research investigates the multifaceted economic and social impacts of business incubation initiatives and their significance. By fostering a conducive environment, business incubators play a critical role in driving economic diversification. It highlights the role of incubators in fostering community engagement by creating networks of entrepreneurs, mentors, and investors that bolster the entrepreneurial ecosystem. The qualitative research methodology involves expert interviews to gather insights and support for the hypotheses formulated. By analyzing findings from various studies, this research offers valuable insights into the complex and multifaceted impacts of business incubation programs. These insights inform policymakers, practitioners, and stakeholders about effective strategies for supporting entrepreneurship and fostering sustainable development both locally and globally.
Antecedents and Outcomes of Knowledge Sharing in Business Incubators From Social Capital Theory's Perspective
Authors: Zibarzani Masoumeh , Rozan Mohd Zaidi Abd
Published: November 2024

Knowledge sharing (KS) is referred to as an important strategy for improving innovation, productivity, efficiency, and competitiveness of organizations. The outcomes of KS are explained from different standpoints. However, previous studies significantly fail to explore the outcomes of KS from a relationship marketing (RM) perspective. This study examined the outcomes of KS on an organization's performance and relationship with customers in business incubators in Malaysia drawing on the social capital theory and RM concepts. The study used a quantitative PLS-SEM approach and using a cross-sectional survey method data was collected from 104 randomly selected respondents. Results reveal that the overall relationship between KS and the customer relationship is significant. The findings indicate that the KS has a positive effect on customer relationships and work performance. This research expands the understanding of the effects of KS on organizations from the RM perspective in business incubators in Malaysia.
Get-Set-Go- Blockchain Startup
Authors: Gudimetla Satya Sekhar Venkata
Published: November 2024

‘Blockchain Startup' is an innovative business idea which works with the decentralized financial (Defi) transactions through an encrypted system with the help of initial coin offering (ICO). The ICO is a way for startups and organizations within the crypto market to raise funds through unregulated crowdfunding. Blockchain is technology is used for cryptocurrency as an open-source currency – functioning through a central distribution agency or state lead control. Cryptocurrency helps in reducing intermediation of banking sector. Hence, protecting the Defi-transactions from hacking depends on the incentive-compatible proof-of-work (PoW). However, investors are crazy about digital currency/ crypto currency trading irrespective of risks involved. This chapter addresses the issues 1) to make aware of the status, progress of blockchain startup ventures and 2) to understand the threats and challenges involved in blockchain-startup business.
Blockchain for Banking and Finance
Authors: Shah Manita D. , Seranmadevi R. , Jose Shruti , Tyagi Amit Kumar
Published: November 2024

Blockchain technology (BT) has emerged as a transformative effect in the banking and finance sector, promising increased efficiency, transparency, and security in financial transactions and services. This paper provides a detailed overview of the applications, challenges, and opportunities of blockchain technology in the banking and finance industry. We discuss about the fundamentals of blockchain, highlighting its key features such as decentralization, immutability, and smart contracts. This chapter provides various use cases of blockchain in banking, including cross-border payments, trade finance, and digital identity verification. We discuss the potential impact of blockchain on financial intermediaries, regulatory compliance, and risk management. While acknowledging the promising benefits of blockchain, we also discuss the challenges and limitations, such as scalability issues and regulatory issues, that must be addressed for widespread adoption. This work serves as a valuable resource for researchers, etc., can found valuable information of the transformative potential of BT.
Leveraging Knowledge Management for Sustainable Development
Authors: Martínez-Falcó Javier , Sánchez-García Eduardo , Marco-Lajara Bartolomé
Published: November 2024

This research aims to investigate the impact of Knowledge Management (KM) on Sustainable Performance (SP) in the wine industry. The study specifically examines how KM influences SP, with a focus on the mediating role of Green Innovation (GI) and the moderating role of Collaborative Culture (CC). A theoretical framework based on existing literature is utilized and tested empirically through structural equation modeling (PLS-SEM) using data collected from 202 Spanish wineries. The results reveal a positive association between KM and SP, with GI serving as a mediator and CC acting as a moderator in this relationship.
Eco-Friendly Synthesis and Characterization of Gold Nanoparticles Using Leaf Extract of Eucalyptus globulus
Authors: Waghmode Samadhan , Ghadage Sushilkumar , Sonawane Anup , Zanan Rahul
Published: November 2024

An essential component of nano-biotechnology is the production of reliable and eco-friendly routes for synthesizing nanoparticles. Plant extracts are an affordable, cost-effective and eco-friendly material for the synthesis of nanoparticles. In the current study, gold nanoparticles (AuNPs) were bio-synthesized utilizing E. globulus leaf extract. Rapid reduction of the gold ions into AuNPs is seen once the gold ions are exposed to an aqueous solution of leaf extract. TEM, EDX and UV-Vis spectroscopy methods were used to investigate these bio-synthesised AuNPs. The aqueous media containing AuNPs had a peak in the UV-visible spectrum at 540 nm. According to TEM investigation, AuNPs were evenly dispersed and had spheres with spherical and triangular forms that were between 15 and 30 nm in size. By using the agar well diffusion method, the antibacterial activity of the biosynthesized AuNPs was confirmed against prevalent human pathogens such S. aureus (NCIM 5021) and P. aeruginosa (NCIM 5029). This process uses cheap, easily accessible biomaterial to synthesize metallic nanoparticles.
Harnessing the Therapeutic Potential of Metallic Bionanoparticles
Authors: Hade Swati Nilkanthrao
Published: November 2024

The integration of nanotechnology and biological systems has led to the emerging field of nanobiotechnology, particularly in the development of nanomedicine. This chapter demonstrates that MNPs synthesized from plant extracts, such as silver, gold, copper, and zinc oxide nanoparticles, have significant therapeutic potential. Their antioxidant properties are crucial in mitigating oxidative stress-related diseases, while anticancer activities facilitate targeted drug delivery and enhanced cytotoxic effects against cancer cells. Additionally, antimicrobial properties offer a promising alternative to combat antibiotic-resistant pathogens. Overall, the green synthesis of MNPs represents a promising advancement in nanomedicine, combining traditional medicinal knowledge with modern nanotechnology. This approach has the potential to revolutionize drug delivery systems, reduce the side effects of conventional therapies, and contribute to the development of new, effective treatments for various diseases.
Green Synthesis of Nanoparticles and Their Conjugations With Antibiotics for Treating Bacterial Infections
Authors: Srivastava Jyoti , Siddiqui Khushbu Iqbal , Kurrey Nawneet , Shabab Mohammed
Published: November 2024

The escalating threat of antibiotic resistance poses a formidable challenge to the global healthcare system, necessitating novel approaches to combat multidrug-resistant bacterial infections. The synthesis of nanoparticle antibiotic conjugates emerges as a promising avenue. Nanoparticle antibiotic conjugates represent a groundbreaking strategy that harnesses the unique properties of nanoparticles and antibiotics to enhance antimicrobial efficacy. This chapter delves into the innovative fusion of nanoparticles and antibiotics through green synthesis techniques, aiming to address the pressing challenges of antibiotic resistance. By exploring these hybrid nanostructures' synthesis, characterization, and therapeutic potential, this chapter sheds light on their significance in revolutionizing infectious disease management. This article highlights potential mechanisms of action of Nanoparticle antibiotic conjugates as next-generation antimicrobial agents and future implications and challenges.
Big Data Analytics
Authors: Chairmadurai K. , Srinivasan G. , Sekar G. , Chinnathambi Dhaya , Jayanthi A. , Bharath Kumar B.
Published: November 2024

Medical difficulties like Alzheimer's Disease require improved biomarker finding research. Alzheimer's requires complex methods to find pre-symptomatic markers and improve early diagnosis. Machine Learning and Deep Learning methods like Recurrent Neural Networks (RNN) may help solve these problems. Due to data complexity and training efficiency, optimizing RNN models for Alzheimer's biomarker analysis remains difficult. This research optimizes training and model performance using Stochastic Gradient Descent (SGD) to address these issues. Clinical, genetic, neuroimaging, and digital biomarker data are integrated using Big Data Analytics methods, particularly Multi-Modal Data Fusion. This fusion technique improves accuracy and prediction by examining Alzheimer's biomarkers holistically. This study shows considerable Alzheimer's biomarker discovery advances. The ML, DL, RNN, SGD, and Multi-Modal Data Fusion technique improves early diagnosis models and risk assessment tools. This research sheds light on using sophisticated technologies to better understand and treat Alzheimer's.
Harnessing Big Data for Early Detection and Progression Tracking of Alzheimer's Disease
Authors: Annie Silviya S. H. , Gethara Gowri C. , Yogashree G. S. , Seedha Devi V. , Kalaivani E.
Published: November 2024

Early detection and monitoring of Alzheimer's disease (AD) are critical challenges in neurology. This research explores the transformative potential of big data analytics to address these challenges. By integrating extensive datasets from diverse sources—genomic data, electronic health records (EHRs), neuroimaging, and patient lifestyle information—we aim to identify early biomarkers and track disease progression with high precision. Advanced machine learning techniques, including deep learning and ensemble models, are applied to uncover hidden patterns and correlations that traditional methods may overlook. Our models demonstrated high accuracy in identifying early biomarkers, with a diagnostic accuracy of 94% (AUC) and an ability to predict disease progression with 92% accuracy. The study also highlighted a 35% improvement in early diagnosis rates and a 20% slower progression rate in monitored patients. Additionally, the importance of data preprocessing, feature extraction, and model interpretability is emphasized to ensure reliable and actionable insights.
Unveiling Alzheimer's
Authors: Annie Silviya S. H. , Sunitha T. , Hilda Jerlin C. M. , Sampath Nithya , Thiruselvan P.
Published: November 2024

In the intricate labyrinth of Alzheimer's disease (AD), biomarkers emerge as guiding stars, illuminating the path toward understanding and combating this formidable foe of cognition. This paper embarks on a voyage through the realms of biomarker research, navigating the intricate landscape of AD diagnosis and progression. From the celestial realms of imaging markers to the molecular depths of biochemical signatures, we delve into the essence of AD pathology. Like skilled cartographers, we map the terrain of early detection, shedding light on the subtle nuances that herald the onset of cognitive decline. As vigilant sentinels, we stand watch over disease progression, discerning the shifting tides of neurodegeneration. Through the kaleidoscope of current research, this paper crafts a mosaic of knowledge, unveiling the potential of biomarkers to guide clinical practice and illuminate the path toward therapeutic horizons in the realm of Alzheimer's.
Novel Approaches for Feature Extraction and Representation Learning of Alzheimer's Biomarkers
Authors: Saini Aradhna
Published: November 2024

Alzheimer's disease (AD), a severe and complex neurodegeneration disease, are relatively invasive and expensive. They depend on measures of amyloid-β1-42 (Aβ42), general tau protein, and hyperphosphorylated tau (p-tau) in cerebrospinal fluid (CSF), in addition to cognitive tests and imaging methods. The biomarkers linked to Alzheimer's disease (AD) are essential for the early diagnosis and therapeutic intervention, the recognition and examination. This work leverages the latest developments in machine learning and data analytics to provide unique methods for feature extraction and representation learning of Alzheimer's biomarkers. Alzheimer's disease is a complicated neurological condition for which there are currently no reliable diagnostic techniques. The methods used now rely on imaging, cognitive assessments, and biomarkers found in the cerebrospinal fluid (CSF), such as amyloid-β1-42 (Aβ42), phosphorylated tau (p-tau), and total tau. These methods are expensive and intrusive, and they have limitations with regard to sensitivity and specificity.
The Role of Biomarkers in Alzheimer's Disease Progression
Authors: Batta Priya , Chauhan Rakhi
Published: November 2024

The research examines various approaches and analyses that by using a hybrid approach for Alzheimer's disease diagnosis, the early detection and efficiency can be achieved. The important neuroimaging methods that provide deep knowledge of the structure and function of the brain include PET scans, structural MRIs, and functional MRI. The main symptoms of biomarkers from blood and brain fluid samples are the beta-amyloid and tau proteins. In order to improve the accuracy and treatment of disease, these techniques along with various machine learning models can be used. The study shows that by using a hybrid framework Alzheimer's disease diagnosis and treatment can be done in well-organized manner.
Exploring the Earth From Afar
Authors: Dubey Kumud , Vandita
Published: November 2024

With environmental concerns on the rise, the integration of modern technology with environmental sciences provides large scale and effective monitoring of the natural resources. This chapter aims to provide the basic understanding about Remote Sensing (RS) and Geographic Information Systems (GIS), thereby exploring, in depth, the applications of RS and GIS in environmental monitoring. It provides a comprehensive overview of the principles, methodologies, and applications of RS and GIS in environmental monitoring, and offers insights into the practical implementation of these technologies across diverse environmental contexts. Recognizing the interdisciplinary nature of environmental monitoring, the chapter explores the integration of technology with other scientific disciplines like ecology, hydrology, climatology, biodiversity conservation, and disaster management. The chapter also covers a case study on “Decadal Vegetation Cover Change Study in the Forest Near Sandstone Mines in Shankargarh, Prayagraj, India”, which dives into the impact of sandstone mining on the vegetation cover of nearby forest.
Comparison of Photogrammetric Reconstruction Methods
Authors: Tychola Kyriaki A. , Vrochidou Eleni , Papakostas George A.
Published: November 2024

Digital photogrammetry is a passive contactless, easy, and low-cost 3D scanning technique, that provides effective and powerful tools for 3D realistic reconstruction, as well as precise and high-resolution models based on Structure from Motion (SfM). Photogrammetry leverages data obtained by Unmanned Aerial Vehicles (UAVs), which have reached significant maturity in the sense that they offer a fast, affordable, and effective method for the acquisition of high-resolution geospatial information and image block (nadir and oblique) with high geometric and temporal resolutions. Data are processed by different available commercial or open-source photogrammetric software aiming at 3D reconstruction modelling. Then, the 3D-generated models can be further processed for quantity assessment by applying geometrical measurements. In addition, models through various geovisualization techniques can be used for realistic representations of the physical world. The current research investigates 3D model reconstruction by applying two photogram-metric software, Agisoft Metashape and 3DF Zephyr, in an example of an archaeological site in Greece focusing on the performance of each approach. Moreover, different distance and noise metrics are applied to compare and geometrically assess the 3D dense point clouds, meshes and Digital Surface Models (DSMs). Furthermore, traditional, and modern geovisualization forms of 3D models are discussed towards approaching the 3D models' representation holistically, aiming to provide guidelines to researchers in this field through a comprehensive workflow from data acquisition to manipulation and capabilities combined with other fields such as Computer Vision. Finally, open issues and future research directions are highlighted as a stimulus to further investigations.
Analyzing Traffic Congestion Patterns in M'diq
Authors: Lahlouh Ayoub , Ben Driss Nisserine , Cheikh Sanaa
Published: November 2024

This study integrates topographical, geographic, and infrastructure data into a GIS framework to investigate the complex dynamics of urban traffic congestion in M'diq. The research emphasizes common causes of congestion, including urban morphology, road network connectivity, public service distribution, and the impact of seasonal tourism. A composite heat map is utilized to identify heavily congested areas, such as Lalla Nezha and Nasr Streets. The study elaborates on the historical growth patterns, geographical difficulties, and randomness of the road networks, all of which exacerbate traffic issues. Oriented mainly toward the analysis of the causes of congestion, this research puts a spotlight on data-driven approaches to urban planning as a means to handle them. Findings feed into larger discussions of urban traffic management by underscoring the need for strategies in innovative planning, taking into account M'diq's unique topographical and infrastructural characteristics.
Application of Cloud-Based Remote Sensing With Google Earth Engine in Land Cover Change Analysis
Authors: Nguyen Liem Duy , Pham Ngan Thi Thu
Published: November 2024

This chapter reviews research on the Google Earth Engine (GEE) platform and includes a case study in Vietnam. The literature highlights GEE's transformative role in geo-big data analytics, with its scalable and cost-effective processing of large datasets, despite some limitations in spatial functions and algorithms. The study employed GIS and remote sensing with GEE to generate land cover maps for 1985, 1995, 2005, 2015, and 2022, analyzing changes over this period. Using Landsat imagery and the random forest algorithm, the study achieved high classification accuracy, with overall accuracies above 96% and Kappa coefficients over 0.95. Findings show a shift from bare land and forest to increased crop areas and reduced forest cover due to socio-economic factors. Water bodies initially shrank but later expanded with irrigation development. The study provides insights for land management, while noting limitations like satellite resolution and weather conditions, and suggests future research should incorporate high-resolution data and additional factors affecting land cover changes.
Evaluation of the Digital Marketing Technologies Using the Logarithm Methodology of Additive Weights in Fuzzy Environments
Authors: Aouadni Sourour , Aouadni Ismahene
Published: November 2024

Customers' buying habits and the marketing tactics businesses employ, including digital marketing, have changed because of the new way of living and the migration of many customers into the virtual world. In this study, the authors are interested in evaluating digital marketing technologies (DMT) using the fuzzy logarithm methodology of additive weights (FLMAW). This study's primary contribution is the fuzzy LMAW model that it suggests for planners and decision-makers to use when evaluating and choosing DMT. Additionally, a real case in a Saudi food company is conducted to demonstrate the viability of the suggested strategy. The FLMAW outcomes were contrasted with those of FTOPSIS and FCOPRAS techniques to illustrate the effectiveness of which had been proven. The sensitivity of the FLMAW method was compared to the FTOPSIS method in the second stage using the rank reversal problem. Lastly, the FLMAW approach yields reliable results.
A Critical Review of Multi-Criteria Decision Analysis Towards Waste-to-Energy Site Selection
Authors: Aouadni Ismahene , Aouadni Sourour
Published: November 2024

The goal of waste management in many nations is moving closer to a circular economy, which aims to create a sustainable society with lower levels of waste generation, fewer landfills, and higher recycling rates. The waste-to-energy (WTE) project's ability to use resources and be safe has made it an essential component in the development of renewable energy. This chapter's review centers on the waste-to-energy site selection issue from 2010 to 2024. It presents a new framework and categorization of the current research streams as well as the many MCDM techniques that have been employed.
Integrating Taguchi Optimization for Multi-Criteria Decision Making in Engineering Applications
Authors: Wadhwa Amandeep Singh , Abbass Mahapara , Akhai Shalom , Kumar Dinesh , Kumar Pardeep
Published: November 2024

This chapter discusses the use of Taguchi optimization, a statistical method for process optimization in engineering, to solve multi-criteria decision making (MCDM) problems. It focuses on achieving robust designs by minimizing variations and defects by identifying optimal control factors. The method uses orthogonal arrays for efficient experimentation and signal-to-noise ratios for performance measurement. It incorporates utility concepts, weighted principal component analysis, and multi-objective optimization. It has real-world applications in automotive, electronics, and chemical engineering. Taguchi's efficiency and cost-effectiveness are compared to response surface methodology and genetic algorithm optimization. It reduces experimental runs, improves product quality, and effectively handles MCDM problems. Future advancements could involve machine learning integration and broader application in emerging fields.
A Hybrid Method for Determining the Export Sector and Product for Turkey
Authors: Ozden Erdemalp , Yaprakli Sevda
Published: November 2024

Developing economies need to support strategic sectors that will transform their productive structures to ensure development. Therefore, the ability of these strategic sectors to produce exportable products is essential. This study follows a different path to determine strategically important export sector and products for Turkey, one of the countries struggling with the middle-income trap for a while. This different path is a hybrid method by combining the results of Bayesian econometrics with the analytical hierarchy process (AHP) method, one of the multi-criteria decision making (MCDM) methods. As a result, the export sector that should be prioritized is transportation, and export products are cars and vehicle parts. By applying the hybrid method in this study to other countries, strategic sectors and goods can be determined.
Extended Goal Programming Approach for Solving Multi-Objective De Novo Programming Problems
Authors: UMARUSMAN Nurullah
Published: November 2024

A new proposal has been made in this study to solve the multi-objective de novo programming problem using extended goal programming, which includes minmax and weighted goal programming. In the proposal, goal functions are created by fixing each objective function of the multi-objective de novo programming model to its own positive ideal solution. Additionally, the normalization constant is determined using positive and negative ideal solutions in the proposal. The proposed approach made it possible to determine the range in which “efficiency and equity” are balanced in the transition from max. efficiency to max. equity. The applicability of the proposed approach is demonstrated in an illustrative example, and considering the obtained results, the optimal point that represents both poles of the extended GP equally is obtained. Through this optimal point, the optimal feasible amount of each constraint is calculated from the extended goal programming perspective.
Emotional Intelligence
Authors: Lourenço Abílio , Paiva Maria Olímpia , Valente Sabina
Published: November 2024

In building organizational capacity and strategic management in higher education, emotional intelligence is crucial, particularly in promoting diversity, equality, and inclusion. Emotional intelligence skills shape values and behaviours, making them essential for creating a positive and inclusive organizational climate. Integrating emotional intelligence into development programs in higher education helps foster key skills such as effective communication and conflict resolution. This approach not only strengthens the organizational capacity of higher education institutions but also creates an environment that promotes inclusion and equity. The aim was to create a space for reflection for higher education leaders and managers, highlighting emotional intelligence as the foundation for strengthening organizational culture and enhancing strategic management. Several proposals for implementing emotional intelligence are presented, aiming not only to strengthen the institution as a whole but also to promote a healthy and productive work environment for all involved.
The Impact of Global Events on Hotel Occupancy and Tourist Flows
Authors: Talukder Mohammad Badruddoza , Hoque Mushfika , Miah Titu
Published: November 2024

The hospitality and tourism industries are a critical component of the world economy. However, it is also significantly impacted by world events such as major sporting events, natural catastrophes, pandemics, and economic downturns. This study examines how big events, like the Olympics, impacted by the COVID-19 pandemic, affect tourist behavior and hotel occupancy rates. The study aims to provide valuable insights to stakeholders so they may develop strategies to enhance resilience and capitalize on opportunities by analyzing these repercussions. The three main objectives are finding new travel trends, examining how events impact travel patterns, and evaluating how economic downturns impact traveler behavior. This study offers vital guidance for academics navigating a rapidly changing environment, government decision-makers, and practitioners in the sector.
Role of Visual Image of Hospitality Industry in Promoting Tourism and Decision-Making Process of Consumers
Authors: Mehta Harsh
Published: November 2024

The purpose of this study is to investigate the exploratory role of visual imagery in the hospitality sector and its influence on promoting tourism as well as the decision-making processes of consumer perceptions. By reviewing relevant literature, the authors explore the use of visual images and how these can be used presently and propose a model that explains decision-making within hospitality and tourism promotion. The findings highlight the importance of visual imagery for managing and marketing hospitality and tourism products more effectively. The study also offers a practical model that will serve as a foundation for further refinement through practitioner input. There has been limited scholarly investigation in this field, thus the study addresses a gap in research, merging image formation with practical applications for hospitality and tourism organizations.
The Psychology of Ethical Marketing
Authors: Jindal Priyanshi , Gouri Harshit
Published: November 2024

This research paper examines the interplay between psychology and ethical marketing, focusing on the challenges and opportunities in this field. It explores how consumers' values, emotions, and cognitive processes influence ethical purchasing decisions and the obstacles marketers face in presenting their brands authentically. The study addresses consumer trust, the pitfalls of greenwashing, and cultural impacts on ethical choices. Additionally, it highlights opportunities for businesses to meet ethical consumer demands, foster brand loyalty, and enhance social responsibility. By analyzing successful ethical marketing campaigns and psychological tactics, the paper offers practical insights for businesses to align their practices with ethics, emphasizing authenticity, transparency, and emotional engagement. It calls for further research to advance understanding in this area, promising a brighter future for both businesses and society.
Memorable Tourism Experiences (MTEs) and Its Applications in the Indian Hospitality Industry
Authors: Chakraborty Oindrila
Published: November 2024

The chapter will focus on how India's tourism industry can create lasting and enjoyable experiences through Memorable Tourism Experiences, covering areas such as hotels, restaurants, themed tourism, and guided tours. The authors through the research would aim to gather insights from both regular travellers within and outside the country, as well as hotel owners, restaurant managers, tour operators, and travel agents through two separate surveys, examining tourists' attitudes towards memorable tourism experiences(MTEs) and how businesses are implementing MTEs to enhance their sustainability and profitability.
Enhancing Guest Experience Through Smart Hotel Technologies
Authors: Talukder Mohammad Badruddoza , Rahman Md Zubair , Khan Musfiqur Rahoman
Published: November 2024

In an era of swift technological advancement, the hotel sector is constantly looking for novel approaches to improve visitor experiences. The integration of cutting-edge hotel technologies to improve visitor satisfaction and optimize operations is examined in this abstract. A variety of solutions are included in innovative hotel technology, such as AI-powered assistants, mobile apps, IoT devices, and automated systems. These technologies provide individualized experiences, effective services, and seamless interactions. Hotels may anticipate visitor preferences and customize services accordingly by utilizing data analytics, which promotes guest loyalty and positive ratings. Intelligent technologies lower operating costs, improve resource management, and support sustainability initiatives. Nonetheless, issues like maintaining a personal touch and data security are still relevant. Notwithstanding these difficulties, implementing cutting-edge hotel technologies offer tremendous potential for revolutionizing visitor experiences and establishing new benchmarks for superior hospitality.
Ancient Wonders, Modern Tech
Authors: Al-Romeedy Bassam Samir , Hussein Hamada
Published: November 2024

Egypt's ancient heritage sites are a major draw for global tourism, attracting millions of visitors each year. However, effectively managing and enhancing the visitor experience at these complex and historically significant locations presents ongoing challenges. This chapter explores how artificial intelligence (AI) technologies can be leveraged to improve various aspects of the tourist experience at Egypt's heritage sites. Strategic implementation of AI can significantly enhance visitor experience, satisfaction, and overall engagement at Egypt's archaeological treasures. Recommendations are provided for policymakers, tourism authorities, and industry stakeholders to effectively integrate AI capabilities while preserving the integrity of these irreplaceable cultural assets. The chapter contributes important insights into the evolving role of AI in elevating the tourist experience at the world's most iconic heritage destinations.
Medical Tourism in India
Authors: Sharma Eliza
Published: November 2024

The study aims to identify factors driving medical tourism in India and assess its scope from the perspective of foreign tourists. Data were collected from 150 foreign tourists seeking medical treatment in hospitals across New Delhi, Mumbai, and Bangalore using a questionnaire. The study highlighted total ten factors which attract the foreign tourists to see India as medical tourism destination. These factors are mainly healthcare infrastructure, healthcare professionals, quality of healthcare services, cost of medical services, transportation connectivity, documentation processes, Government support for medical tourism promotion, Technological innovations, medical insurance and tie ups for medical services and local or regional factors. The cost of medical services was identified as a major factor, while government support, healthcare infrastructure, and other factors need improvement. The study suggests that India's potential as a medical tourism destination can be enhanced by improving the healthcare, transportation, hospitality, infrastructure and governmental support.
AI, Blockchain, and Cybersecurity
Authors: K. M. Shashank Kamath , Parkar Trupti Vinayak
Published: November 2024

The exponential growth and complexity of healthcare data demand innovative solutions to safeguard its integrity, security, and utility. This chapter will delve into the cutting-edge intersection of artificial intelligence (AI), blockchain, and cybersecurity in transforming data integrity and security within the healthcare industry. As the healthcare sector increasingly relies on vast, sensitive datasets, the integration of these technologies is critical for ensuring secure, efficient, and trustworthy data management. By delving into the challenges posed by the exponential growth of healthcare data, the chapter will demonstrate how these technologies can revolutionize data integrity, security, and accessibility. Key areas of focus include how AI enhances data accuracy and accessibility, blockchain provides immutable data storage and traceability, and the critical importance of robust cybersecurity measures protect sensitive patient information against evolving threats.
Informatics and Internet of Things Uses in Clinical Medicine
Authors: Sree M. Swathi , Kishor Kumar Reddy C. , Vaishnavi Kurella , Harika Vallapureddy
Published: November 2024

Clinical medicine's use of informatics and the Internet of Things (IoT) is revolutionising the delivery of healthcare by enabling remote monitoring, real-time data collection, and individualised patient care. To provide ongoing health monitoring, early diagnosis, and prompt therapies, this strategy combines Internet of Things (IoT) devices with sophisticated informatics technologies, such as data analytics and artificial intelligence (AI). Vital health data is collected by Internet of Things (IoT) devices, such as wearable sensors and smart medical equipment. Cloud-based systems process and use artificial intelligence (AI) algorithms to analyse the data. The aforementioned technologies facilitate telemedicine, augment the management of chronic illnesses, boost patient outcomes, and optimise clinical operations. Nonetheless, major obstacles continue to be issues like data privacy, security worries, and the requirement for a strong infrastructure.
Medical Imaging and Artificial Intelligence
Authors: Abraham Sujan , Joseph Shenson
Published: November 2024

The rapid advancement of artificial intelligence (AI) is transforming medical imaging in mainly healthcare by integrating cutting-edges technologies such as deep learning models and convolutional neural networks. This evolution has revolutionaries all the analysis of medical images, while leading to faster, more accurate diagnoses. As a result, early detection of critical conditions like cancer, cardiovascular diseases, and neurological disorders which has majorly improved, providing patients with timely treatment options and that enhance survival rates and quality of life. The main research highlights how AI and digital tools have not only increased diagnostic precision but also streamlined clinical workflow, while allowing healthcare providers to mainly focus more on complex decision-making. Additionally, the main study addresses challenges related to AI in clinical practice, such as data quality, model bias, and ethical issues, and demonstrates how all of these challenges have been addressed through robust validation and cross-institutional collaboration.
Predictive Analytics in Emergency Services
Authors: Ordu Muhammed , Demir Eren , Tofallis Chris
Published: November 2024

This study seeks to identify the most effective forecasting period and methods for predicting demand in an Accident & Emergency (A&E) department at a mid-sized hospital in England. Utilizing the National Hospital Episode Statistics (HES) dataset, that covers a 36-month period from February 2010 to January 2013, the research evaluates four commonly used forecasting methods: Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, stepwise linear regression (SLR), and Seasonal and Trend decomposition using Loess (STLF). Forecast accuracy is assessed using the Mean Absolute Scaled Error (MASE). The MASE values for the best forecasting methods across different periods were 0.7834 for daily, 0.9354 for weekly, and 0.5259 for monthly estimates. The study found that the SLR model was the most effective predictive method, with monthly estimation emerging as the optimal period. Contrary to past studies that favoured daily estimates, this research indicated that daily A&E demand forecasts might not be the most accurate.
An Innovative Method for Real-Time Eye State Detection in Fatigue Monitoring Systems
Authors: Telagarapu Prabhakar , Prasad Chapa Babji , C. Kishor Kumar Reddy
Published: November 2024

Driver fatigue is a leading cause of automobile accidents. Automated vision-based detection of driver fatigue, grounded in facial expression analysis, is an emerging commercial application. Among the facial features, the eyes are especially critical for detecting drowsiness. In this study, we introduce a system that monitors facial activity, focusing on the eyes, to detect signs of sleepiness. The system analyses a sequence of images of the driver's face, captured by a video camera, and evaluates fatigue based on eye movement and eyelid position. Tested in a car simulation environment, the system identifies the status of the eyes by extracting characteristic parameters to detect fatigue.
Artificial Intelligence-Based Healthcare Systems for Pets and Birds
Authors: Ayyaswamy Kathirvel , Gobinath V. M. , Kathirvel Naren , Saravanan N.
Published: November 2024

AI-based healthcare systems use sophisticated algorithms and machine learning techniques to analyze, interpret, and process large amounts of animal health data.Veterinarians and pet owners will benefit from these systems, which provide accurate diagnoses, tailored treatment plans, and proactive health care.Veterinary medicine has witnessed a revolution thanks to the integration of Artificial Intelligence (AI) into pet and bird healthcare systems. A machine-learning-based healthcare system analyzes, interprets, and processes large amounts of animal health data using sophisticated algorithms and machine-learning techniques. Pets and birds cannot express their discomfort or symptoms like humans can, which makes it difficult to identify potential health problems at an early stage.
Predictive Model Approach for Enhancing Diet Management for Diabetes Patients Through Artificial Intelligence
Authors: Bhadouria Aashi Singh , Ahirwar Anamika
Published: November 2024

Diabetes represents a severe global health crisis with escalating rates, complications, and economic impact. Effective management requires a combination of nutrition, physical activity, medication, and insulin therapy, but challenges like limited specialist access and medication adherence hinder optimal glycemic control. Recent advancements in digital health, especially artificial intelligence (AI), offer promising solutions. This study explores the integration of AI in diabetes management through a Random Forest classifier to provide personalized dietary recommendations. The Nutrition Diet Expert System (NDES) achieved impressive results with 96.48% accuracy, 0.98 precision, 0.96 recall, and 0.97 F1-score. By optimizing food intake, insulin management, and lifestyle adjustments, NDES supports stable blood glucose levels, healthy weight, and improved patient out-comes. Ongoing AI advancements continue to offer innovative strategies for tackling global diabetes challenges.
Examining NLP for Smarter, Data-Driven Healthcare Solutions
Authors: Upadhyaya Nitesh , Joshi Herat , Agrawal Chetan
Published: November 2024

This chapter delves into the critical role of Natural Language Processing (NLP) in the healthcare sector, with a focus on its current applications and future potential. It examines how NLP enhances clinical documentation, decision support systems, and patient-provider communication. The chapter also examines problems such as data privacy, security, bias, and model interpretability, which prevent NLP from being fully integrated into healthcare systems. Solutions such as explainable AI, regulatory compliance, and interdisciplinary collaboration are proposed to overcome these barriers. The chapter further explores advancements in deep learning models, cross-language NLP, and predictive analytics that are poised to revolutionize healthcare by providing more personalized, data-driven care. Overall, the chapter emphasizes NLP's transformative potential in healthcare, as well as the ethical and technical problems that must be addressed before it can completely fulfill its benefits.
Harnessing Quantitative and Qualitative Data for Digital Health Experience Design
Authors: Ramamoorthy Nithhyaa
Published: November 2024

With the consistent growth of digital health adoption following the COVID-19 pandemic, there are numerous product offerings available for Patients, Physicians, Researchers, and many more audience groups. Data and design practitioners are increasingly focusing on designing digital experiences that are deeply user-centric. The result has been the development of websites, applications, and other patient-facing digital experiences developed around principles of user-centricity The abundant sources of data available in the current digital landscape are often the foundational resource and the fuel for AI-empowered healthcare solutions. One crucial factor that determines the adoption of these data-driven solutions is the experience design of these digital health products. In the chapter, readers will learn about the impact and for Human Centered Design and how to harness various types of Data-driven User behavior research to fuel Digital Health Experience Design.
Harnessing the Power of Large Language Models for Cybersecurity
Authors: Zangana Hewa Majeed , Omar Marwan
Published: November 2024

The LLMs not only have changed the overall nature of NPL but have also helped a lot in setting standards in cyber security. Within the confines of this review, the authors discuss the benefits, progressions, difficulties, as well as the future paths aimed to be taken in the cybersecurity field of LLMs. They delve into how LLMs help companies process unstructured textual data for text dangers detections, vulnerability assessments, and incident responses. In addition, they investigate the ethical and societal consequences of using LLMs for cybersecurity, facing challenges like algorithmic bias, privacy, and data safety. Besides that, they find that critical research questions in the crossroads of LLMs and cybersecurity language include unique assessing techniques and the improvement of algorithms to clarify the information. Through the development of many-faceted interdisciplinary cooperation and ethics-based considerations, we can maximize the opportunities LLMs present in the cyber world and build a more resilient and secure environment for everyone.
Foundations of Large Language Models in Software Vulnerability Detection
Authors: Zangana Hewa Majeed , Mohammed Derek
Published: November 2024

This chapter explores the foundational aspects of large language models (LLMs) and their application in detecting software vulnerabilities. As the complexity of software systems grows, traditional methods of vulnerability detection are often insufficient. LLMs, with their advanced natural language processing capabilities, provide a novel approach to identifying potential security threats in codebases. The chapter delves into the architecture of these models, their training mechanisms, and the challenges they face in the domain of cybersecurity. Additionally, it discusses the ethical implications and future directions for integrating LLMs in automated software vulnerability detection.