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AI in academia: Balancing integrity, ethics, and learning amid evolving norms of authorship and scholarship
The integration of AI in academic and publication content generation is a recent development, significantly altering policies on citation and authorship, which were previously designed for human-generated work. While AI tools have eased administrative and academic workloads, their rapid adoption raises concerns about ethics and academic integrity. This chapter explores the role of AI as a transformative force in academia, highlighting both its benefits and potential downsides. A key concern is the potential erosion of critical thinking among researchers and scholars due to overreliance on AI, which could impact the quality of research. Despite being a game-changer for students, educators, and administrative staff, the academic community must address the ethical implications and develop new policies to ensure that AI enhances rather than undermines scholarly work. This chapter aims to foster dialogue on how academia can coexist with advancing AI innovations while maintaining research integrity and quality. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Chain funds: Transforming healthcare crowdfunding through blockchain technologies
Crowdfunding in healthcare refers to the practice of raising funds from a large number of individuals through online platform. This will eventually support the various medical expenses involving hospitalization and treatment. This method enables individuals with health problems or an urgent need for medical treatment to go out and seek financial assistance from a large number of people. Crowdfunding on health is now widely recognized as the means of catering for medical bills, surgeries, experimental treatments and other health-related expenses. The research presents a decentralized blockchain based crowdfunding platform built using ReactJS, Solidity and Thirdweb SDK. This new platform aims to change traditional crowdfunding techniques through utilization of the benefits of blockchain such as transparency, security and automation provided by smart contracts. The user interface has been designed using ReactJS; creating smart contracts on the Ethereum Blockchain was done under Solidity; Thirdweb SDK was used to create a link between the system and the blockchain. Some significant features of this platform involve start-up campaigns organization, contribution management, milestone tracking and automatic distribution of funds in order to enhance customer satisfaction and minimize operational costs. Moreover, users are awarded with certificates and crypto tokens. In addition, this policy ensures high level security that comes with immutability in blockchain technology by eliminating middlemen hence live monitoring of fund is possible through it at all times and disbursements. Rigorous testing has been conducted to assess performance, security, and scalability, highlighting the advantages of decentralization in contrast to centralized crowdfunding models. 2025 Author(s). -
The mediating role of self-efficacy and networks in entrepreneurial intentions: Entrepreneurial education, university support, and user innovation in rural India
This research examines the complex relationships between entrepreneurial education, university support, entrepreneurial self-efficacy, network connections, and entrepreneurial intentions in Indian university students. The study uses a quantitative approach and structural equation modeling on a sample of 422 students enrolled in entrepreneurship-related programs to elucidate the mediating roles of entrepreneurial self-efficacy and network connections. Despite the insignificant direct links betweenentrepreneurialeducation/university support and intentions,entrepreneurial self-efficacy and network connections emerge as important mediators. The findings provide theoretical insights into the multifaceted nature ofentrepreneurial intentions and have practical implications for improving entrepreneurship support programs. By understanding these mechanisms, stakeholders can create an environment that fosters entrepreneurial aspirations among university students. 2024 by IGI Global. All rights reserved. -
Women human rights defenders
[No abstract available] -
Allometry Authentication in the Field of Finance: Creation of Well Secured System using AI Algo Based Systems
It is true the banking sector is increasingly under pressure to tighten security in an ever-changing digital arena, even as the customer experience needs to be strengthened. Thus, the use of biometric authentication through enhanced AI-driven systems that would enhance the security protocols while at the same time smoothening the users' interactions was a promising way in response. The paper that follows explores the integration of biometric authentication within banking systems in a bid to make clear its effectiveness in relation to reinforcing security and enhancing user experience. Accordingly, bijson etal. argue that biometric security fits perfectly in banks, since with the increasing cyber threats, banks are bound to deploy more advanced security mechanisms. These traditional means, suchjson, use of passwords and PINs, have shown vulnerabilities that are liable to exploitation and should be changed into something much more resilient. The authentication under biometrics also validates a user's identity by basing it on unique physiological or behavioral traits, such as a fingerprint, features of the face, patterns of the iris, and the voice. Biometric systems authenticate users with a very high level of confidence through AI-based algorithms, averting the security risks associated with unauthorized access and identity theft. Further, biometric authentication overcomes the flaws that prevail with the traditional mode of methods and hence, it ensures a very comfortable and user-friendly mode of system security. 2024 IEEE. -
Quantum-Enhanced Cryptographic Key Exchange for Secure IoT Networks
The coupling of quantum-metering methods with cryptographic key exchanges spells out a new security paradigm model to safeguard Internet of Things (IoT) networks against rapidly changing cyberspace threats. The given assessment explains a quantum-advanced scenario that is operationally feasible and serves as a platform for quantum key distribution (QKD) along with classical post-quantum algorithms to deliver end-to-end confidentiality, integrity, and authentication features to a wide range of IoT devices. The model, by the virtue of quantum entanglement and photon polarization used in the creation of tamper-evident communication links, is invulnerable to adversarial-aided eavesdropping and computational assault methods and further offers a hybrid encryption protocol that has been demonstrated to alleviate key generation and exchange latency trade-offs - simultaneously maintaining scalability and effectiveness in resource-constrained IoT node environments. The key compromise probability is significantly lowered in the experimental results, along with the keys' entropy levels, which were generated in the pure QKD space as opposed to classicalbased RSA and ECC methods. Also, the framework investigates lightweight quantum-safe authentication techniques that can be used to establish trust at the device level. The output points to the enhanced resistance to quantum and classical attackers that can make the solutions work in real-time application IoT environments such as smart healthcare, autonomous systems, and industrial automation. Overall, the quantum-enriched model of cryptographic key exchange is next-gen IoT ecosystem to be implemented subsequently. 2026 IEEE. -
Stroke disease classification from computed tomography images using Inception Harmonic LeNet and wavelet- symmetrically weighted local gradient pattern features
Stroke is a leading cause of mortality, making prompt and precise diagnosis essential for effective treatment. Computed Tomography (CT) screening is crucial in identifying stroke types, particularly ischemic and hemorrhagic strokes. Existing automated methods lack the accuracy and consistency required for reliable stroke diagnosis. Therefore, a novel Inception Harmonic LeNet (InHLeNet) approach is devised for stroke disease classification. Initially, CT scans are collected and subjected to preprocessing, which is done using guided filtering and an improved Non-Subsampled Shearlet Transform (NSST) threshold. The filtered images are then segmented using the Dimension fusion U-Net (D-UNet). Subsequently, augmentation is performed by local augmentation and self-augmentation, where local augmentation introduces localized variations within each CT image, and self-augmentation generates feature-guided transformations of lesion regions. Further, the wavelet transforms with Symmetrically Weighted Local Gradient Pattern (Wavelet-SWLGP) features are extracted. Lastly, stroke disease is classified using InHLeNet, which merges InceptionV3Net, LeNet, and Harmonic analysis. The performance of InHLeNet is assessed using several evaluation metrics, including accuracy, True Positive Rate (TPR), True Negative Rate (TNR), and Matthews Correlation Coefficient (MCC). The results attained using the InHLeNet model is accuracy of 96.888%, TNR of 96.381%, MCC of 96.777%, and TPR of 97.988%, with image size, highlighting its effectiveness. 2026 Elsevier Ltd -
Hybrid shuffled frog leaping and improved biogeography-based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks
Wireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography-based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes. 2021 John Wiley & Sons Ltd. -
Social Network User Profiling With Multilayer Semantic Modeling Using Ego Network
Social and information networks undermine the real relationship between the individuals (ego) and the friends (alters) they are connected with on social media. The structure of individual network is highlighted by the ego network. Egocentric approach is popular due to its focus on individuals, groups, or communities. Size, structure, and composition directly impact the ego networks. Moreover, analysis includes strength of ego alter ties degree and strength of ties. Degree gives the first overview of network. Social support in the network is explored with the gap between the degree and average strength. These outcomes firmly propose that, regardless of whether the approaches to convey and to keep up social connections are evolving because of the dispersion of online social networks, the way individuals sort out their social connections appears to remain unaltered. As online social networks evolve, they help in receiving more diverse information. 2022 IGI Global. All rights reserved. -
Synthesis and characterization of graphene filled PC-ABS filament for FDM applications
Present investigation focuses on development of graphene filled PC-ABS filament for Fused Deposition Modeling applications. Compounding and twin screw extrusion was employed to synthesis graphene filled FDM filament of 1.75mm diameter. Percentage of graphene was varied from 0.1 vol% to 0.25 vol% in steps of 0.05. Developed filaments were subjected to SEM studies, dimensional accuracy and density measurements. In order to achieve filament of 1.75mm diameter, filament extrusion temperature was optimized using Taguchi's L25 orthogonal array, microstructure shows homogeneous dispersion of graphene particles in PC-ABS matrix, density decreases with increased content of graphene particles. 2018 Author(s). -
Synthesis and characterization of flyash reinforced polymer composites developed by Fused Filament Fabrication
Fused filament fabrication (FFF) has seen an upsurge in its utilization towards development of tailored made materials of polymer base. The advancement and diversity in fabricating the polymer composite parts by using FFF has seen the embracement of this technology in wider aspects, ranging from automotive, aerospace, construction and has marched towards day to day requirements. This research article focuses on development of polymer composite; by using flyash (FA), an industrial waste produced during coal combustion, as reinforcement in Acrylonitrile butadiene styrene (ABS) matrix, to study the physical and mechanical properties. FA, which is primarily made up of metal oxides, plays an imperative role as reinforcement. Easily and abundantly available, FA is being used in several applications to reduce the landfills utilization and also helps the environment. In this study FA was added as reinforcement in 5 and 10 wt. % respectively to ABS matrix and was developed into filament of 1.75 mm diameter. The developed ABS + FA polymer composite using FFF, were analyzed for physical and mechanical properties as per American Society for Testing and Materials (ASTM) standards. Microstructure studies were carried out for the developed composite to understand their behavior in enhancing the dimensional accuracy and tensile strength with incremental addition of FA up to 10 wt%. Tensile strength was enhanced by 28.19% and 36.13% for ABS + 5wt. % FA and ABS + 10wt. % FA respectively. Dimensional stability was also enhanced. Similarly, surface roughness analysis was carried out and it was observed to reduce with addition of FA. The surface roughness measurements provided suitable results of decrement by 9.64% and 14.6% for ABS + 5wt. % FA and ABS + 10wt. % FA respectively. Overall, the usage of FA along with FFF, has paved a path in sustainable and green technology in manufacturing. 2022 The Author(s). -
Thermal behavior of PC-ABS based graphene filled polymer nanocomposite synthesized by FDM process
Property enhancement of polymers could be achieved through blending of two or more polymers and via addition of filler materials to meet the application requirements. In the present investigation Polycarbonate (PC) and Acrylonitrile Butadiene Styrene (ABS), the two polymers were blended together and Graphene platelets as nanofiller was added in the ratio of 0.2, 0.4, 0.6 and 0.8 wt% respectively. Polymer blend and graphene platelets were mixed at appropriate temperature and extruded out in the form of filament of 1.75 mm diameter. Filament was used as a feed material for Fused Deposition Modelling (FDM) to develop the test samples. The nanocomposites developed using FDM were subjected to differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) to study the effect of graphene platelets. Addition of graphene platelets resulted in significant increase in Young's modulus with highest value of 4.038 GPa obtained for nanocomposite with 0.8% graphene content. Thermal analysis showed that addition of graphene platelets increases the glass transition temperature and reduces the mass with increase in temperature. 2019 -
Review of Development and Characterisation of Shape Memory Polymer Composites Fabricated Using Additive Manufacturing Technology
Structures as well as components are generated by depositing filaments on one another via the technique of additive manufacturing. Among the various processes of printing, 4D printing combines the technology of 3D printing with the passage of time, resulting in additively generated parts that are responsive to stimuli from the outside via modifications of their form, volume, size, or mechanical qualities. Thus, the materials of shape memory are used in 4D printing and respond to environmental factors including temperature, pH, and humidity. Shape memory polymers (SMPs) are materials with a shape memory effect that are best suited for additive manufacturing. Contrarily, the method named fused filament fabrication (FFF) is employed most frequently among all additive manufacturing methods. In this regard, the objective of the present study is to evaluate all investigations that have been conducted on 4D-FFF materials mechanical properties. The study offers an unparalleled overview that highlights the possibilities of 4D FFF printing across multiple applications in engineering while keeping the end structures or components structural integrity in consideration. 2023 by the authors. -
Prevalence and predictors of diabetes among adults in rural Dharwad, India: A cross-sectional study
Objective: Diabetes is a long life chronic non-communicable disease and emerging fast as one of the most serious health problems in developed and developing countries, also influences the risk of developing macrovascular complication including heart disease and stroke which are the leading causes of global death. This study aims to find the potential risk factors associated to diabetes among different community (Government, Private employees, and Businessmen) of adults 20 years and above. Methods: A cross-sectional study followed and conducted door-to-door survey using World Health Organization STEP Surveillance (WHO STEPS) questionnaire to collect the information of sociodemographic, anthropometric and behavioral characteristics. Multiple logistic regression is used to determine the risk factors of diabetes among study population. Data was pre-processed and used Chi-square test and t-test to find the comparison between the attributes. Results: Overall prevalence of diabetes is found to be 49.1% in which prevalence more in females with 51.7% than in males with 46.8%, the education, health examination, and waist circumference were found to be the potential risk factors. The total study subjects include 1083 in which male is 611 and female is 472. Conclusion: The current study reflects the importance of Diabetes disease among the study population in rural Dharwad and this study can be utilized to control and prevent diabetes. Its an early call for the females of the study population to take care and practice healthy food in day today life and the outcome of the study says that the education should be given prime importance in everyones life. 2018 The Authors. -
Cyber-victimizationinfluence of parental rules and impact on mental health among Indian adolescents
Introduction: In the contemporary digital age, cyberspace offers numerous benefits but also presents significant risks, including cyber-victimization. Adolescents, as frequent internet users, are particularly vulnerable to such experiences. This study examines the relationship between parental regulations on internet usage and the incidence of cyber-victimization among Indian adolescents, while also assessing the impact of cyber-victimization on mental health outcomes such as stress, anxiety, and depression. Methods: A sample of 224 adolescents (Mean age?=?16.5?years SD?=?2.34) was surveyed using standardized measures of cyber-victimization and mental health. Results: Multiple linear regression analyses revealed that written-verbal cyber-victimization was a significant predictor of stress (??=?0.18, p?<?0.05), while impersonation, written-verbal cyber-victimization, and online exclusion significantly predicted anxiety (p?<?0.05). However, none of the cyber-victimization subtypes significantly predicted depression, and the overall model accounted for only 4% of its variance. Discussion: These findings suggest that while cyber-victimization is linked to stress and anxiety, its influence on depression may be more complex. Furthermore, the Pearson correlation analysis indicated a negligible association between cyber-victimization and parental rules on internet usage (r?=?0.039), suggesting that parental regulations alone may not effectively mitigate cyber-victimization risks. Given these findings, interventions focusing on resilience-building, digital literacy, and peer support may be more effective in protecting adolescents from the adverse effects of cyber-victimization. Future research should explore alternative protective factors and preventive strategies to promote adolescent well-being in digital spaces. Copyright 2025 Tamarana, Mathur, Madhusudan and Annapurna Kiranmai. -
Enhancing empowerment: Exploring the influence of tourism social entrepreneurship on community engagement
Social entrepreneurship is an evolving force in solving societal challenges, integrating financial viability with social effect. This study delves into the intersection of social entrepreneurship and tourism, exploring how Tourism Social Entrepreneurship fosters community engagement to boost empowerment within locations. Within the tourism sector, Tourism Social Entrepreneurship attempts to stimulate socioeconomic growth while focusing on the welfare of host communities. Despite its potential, a gap persists in knowing how Tourism Social Entrepreneurship efficiently includes and engages local people. This research aims to fill the void by investigating how social entrepreneurs within the tourist sector engage with and empower local communities. By analyzing current literature on social entrepreneurship and conceptualizing Tourism Social Entrepreneurship, this study provides a complete understanding of the dynamics of community engagement in tourism-driven social entrepreneurship endeavors. 2025, IGI Global Scientific Publishing. All rights reserved. -
Water Demand Prediction Using Support Vector Machine Regression
Water is a critical resource for sustainable economic and social development of a country. To maintain health hygiene, energy agricultural products, and the environment management water plays a key role. Water demand prediction is essential to analyze the requirement that indicate emergency state for water management decisions. This paper explores the water usage data for dairy plants to understand the spatial and temporal patterns for future water requirements, to optimize the water demand estimation. It uses concept of Machine learning algorithms to compare and achieve an effective and reliable system for water prediction. 2019 IEEE. -
Dokmoka Violence: Cultural Cues and Contradictions in News Narratives
This chapter examines misrepresentation and distortion in the news discourse on the 2018 Dokmoka mob lynching reported in the Indian state of Assam. Using narrative analysis, this study conducts an in-depth qualitative investigation of the print media coverage of the event. With a comprehensive methodology and theoretical grounding, this study sheds light on the complex dynamics surrounding the misrepresentation of facts and their implications. It enhances our understanding of news medias challenges in accurately covering events, highlighting the need for critical analysis and responsible reporting in the contemporary media landscape. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Recent Trends in the Synthesis and Mechanistic Implications of Phenanthridines
Phenanthridine derivatives are one of the most intensively studied families of biologically active compounds. There has been considerable scientific interest in the turn of this century in the synthesis of these N-containing heterocycles as they are prevalent in many alkaloids and also possess striking biological activities including antibacterial, antifungal, antitumor activities. In this regard, a number of synthetic approaches toward the construction of phenanthridine moieties with various substituents, and to increase their yield have been developed by the synthetic organic community. Even though many researchers have developed various innovative methods for the synthesis of phenanthridine derivatives for the past few years, still there is substantial scope for the discovery of novel synthetic methods. In this review, the latest developments in the diverse synthetic strategies of phenanthridine derivatives in the presence and absence of metals were described. The present review also enlightens the use of different reagents, the diversity of the substrates identified, and the plausible mechanisms unravelled towards the construction of these biologically relevant scaffolds. (Figure presented.). 2021 Wiley-VCH GmbH -
White Light Emission from Dy3+-Activated CaY2O4 Phosphor
Synthesis and characterization of a Dy3+-activated calcium yttrium oxide (CaY2O4) phosphor are reported. The CaY2O4:Dy3+ (1.5 mol%) phosphor is synthesized using a modified solid-state reaction technique for calcination and sintering. The cubic structure is revealed by the X-ray diffraction technique. The morphology and particle size distribution of the prepared phosphor are investigated by the FEGSEM technique. The chemical bonds and functional group analysis are confirmed by the FTIR. A photoluminescence analysis of the CaY2O4:Dy3+ phosphor shows dual excitation wavelengths at 285 and 348 nm, especially in the ultraviolet region. At 383 nm, three distinct emission peaks are found at the wavelengths 238, 485, and 571 nm. The spectroscopic parameters are calculated using the CIE chromaticity coordinates. The CIE coordinates of the Dysprosium ion-activated CaY2O4 phosphor (1.5 mol%) show an emission near the white light region of the chromaticity diagram, suggesting that it is suitable for W-LED applications. The Author(s), under exclusive licence to Springer Nature Switzlerland AG 2024.