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A novel approach for integrating cryptography and blockchain into IoT system
The quick advancement of Internet of Things (IoT) emphasizes the significance of cryptography and blockchain ensuring the security of sensitive data and connected devices. Blockchain technology and encryption play key roles in ensuring the security of the expansive IoT network. Blockchain offers decentralized trust, immutability, and transparency to IoT networks and transactions, while encryption serves to protect IoT data from unauthorized access. It is a novel approach for integrating cryptography and blockchain into IoT System, cryptography and blockchain stand out as robust technologies that enhance the security of IoT systems. The implementation of an integrated architecture, along with a strategic integration approach, further strengthens the security measures. This methodology proves valuable for managing and validating digital transactions on decentralized, immutable networks. This work also explores the potential significance of integrating cryptography and blockchain into IoT System, this functions and applications in enhancing IoT security. This methodology introduces encryption techniques tailored for resource-constrained IoT devices, which are essential for ensuring end-to-end security. 2024, Taru Publications. All rights reserved. -
Comparing the roles of cryptography and blockchain technology in relation to Internet of Things
Cryptography and blockchain technology are super important for keeping all our smart devices safe in the Internet of Things (IoT) which can be considered as social security. As more and more of our gadgets connect to the internet, we really need to make sure theyre secure. This article talks about how we can use blockchain technology with IoT devices. Introduction and literature survey provides the challenges of having cryptography and blockchain technology in relation to Internet of Things, the proposed methodology provides the limited internet speed, and how blockchain affects how fast our devices can work and compare the cryptography attributes and blockchain technologies with the IoT world. To maintain the data safe and securely and provide the integrity and provide the high throughput, the Weight Allocation Authority (WAA) gives priority to users based on their characteristics. The Secure Hash Algorithm-512 helps make bigger data smaller. This work deals with the Hybrid Weighted Algorithm (HWA) and how IoT devices can improve the faster data transmission securely. WAA and HWA algorithms canalso create a safe and decentralized system that can quickly stop any bad transactions. 2024, Taru Publications. All rights reserved. -
Virtual Reality in Tourism Industry within the Framework of Virtual Reality Markup Language
Virtual Reality (VR) technology has grown and emerged in the tourism industry. It offering immersive and interactive experiences, VR has transformed how people discover and interact with the VRML and people interact with different destinations. This article explores the use of VR in tourism, and focusing on Virtual Reality Markup Language (VRML) and its role in showcasing the evolution of head-mounted displays (HMDs) and the various applications of VR. It emphasizes how VR can improve travel experiences, aid in destination planning, preserve cultural heritage, support adventure tourism, and revolutionize destination marketing. The article also gives the challenges and limitations faced by VR in tourism, as well as future trends and opportunities in the field. The article impact of VR on the tourism industry and discusses the combination of Augmented Reality (AR) and VR to create virtual art exhibitions in physical and online spaces. Additionally, it provides insights into the future of VR, AR, and Mixed Reality (MR), the use of VRML, and the development of 3D modeling for creating virtual environments that help users achieve learning objectives. 2024 IEEE. -
Theorizing race, marginalization, and language in the digital media
Digitization of the communication medium has transformed the mute, marginalized audience into a heterogeneous and credible content producer. Drawing on this dynamics and operation of the digital media, it has urged the need to re-theorize marginalization and race. Hence, this paper critiques the digital-media tool, blogs, using a rhetoric-textual analysis method and critical discourse analysis method for the fictional text, Americanah. These methods employ the psychoanalyticalAlthusserian critique of Adichies fictional narrative, Americanah. In the psychoanalytical sense, blog-writing can qualify as a mechanism of sublimation in the post-modern world. In the Althusserian sense, blogs become persuasive mechanisms for a subjects interpellation into non-dominant ideology. Among the plethora of marginalized global communities, African-Americans are enormously embracing the virtual communication trends for socio-political motives. This paper theorizes the correlations between race-related blogging, psychoanalytic sublimation, and the socio-political repudiation of power structure by employing the literary text as material evidence. Accordingly, the literary study has concluded that digital-mediums (i.e., in this case, political blogs) can depose the power vested in the ideologicalstate-apparatuses and impose a high potential for expression of unrestrained, credible, and democratic voice of the marginalized. It also validates that blogs/blogging influences and moulds national/political/racial discourses by lending a liberated voice and context-independent perspective to the racially oppressed. 2021 Communication & Society. -
You are not Sikkimese enough: Understanding collective action tendencies of old settlers in Sikkim using SIMCA
The current study analyses the motivators and inhibitors of collective action tendency using the Social Identity Model of Collective Action (SIMCA). The study was conducted with a minority and state-based repressed group known as the old settlers in Sikkim, India. The old settlers are a community that have been historically settled in Sikkim prior to the state's merger with India in 1975. They are racially and ethnically different from the majority population of northeasterners in Sikkim and face both institutional and interpersonal discrimination. A qualitative approach using semi-structured interviews with 11 old settlers was taken to delineate SIMCA variables moral conviction, identity, injustice and efficacy within the context of northeast India. Collective action was motivated through moral conviction via principles of equality and unequal treatment and outsider status, identity via politicisation of identity, creation of social movement organisations, injustice via anger and fraternal resentment and efficacy via marches and legal recourses. Collective action was inhibited through moral conviction via denial of violation, identity via acculturation, injustice via fear and efficacy via learned helplessness. These findings indicate that in state-based repressed groups, collective action tendencies must be understood from a context-specific lens that attempts to understand both motivating and inhibitory factors. 2024 Asian Association of Social Psychology and John Wiley & Sons Australia, Ltd. -
Discrimination Experiences of Old Settlers in Sikkim: A Qualitative Exploration
Race-based stigma and discrimination have been extensively studied from the perspective of the northeastern community due to their minority status in most states of India. Discrimination experiences of the mainland Indians in the northeastern states, where they are a minority, are little discussed. The Rajya Sabha (upper house of the parliament) Committee of Petitions in 2014 acknowledged that the old settlers were treated as second-class citizens in Sikkim. In the present study, we explored the existence and manifestation of discrimination experiences of old settlers who settled in Sikkim before 1975 and perceive themselves to be stigmatized. This study focused on Sikkim because the state merged with India in 1975 and has had less time integrating with migrants or mainlanders than other northeastern states. We conducted nine semi-structured interviews with seven male and two female participants from the Marwari, Bihari, and Punjabi mainland communities. Using thematic analysis, we developed 1 global theme, 2 organizing themes, and 24 basic themes. The analysis showed the existence of discrimination and racism against old settlers and their manifestations at institutional and interpersonal levels. The findings are important from a policymaking perspective as they provide evidence to the conclusion reached by the Rajya Sabha Committee on Petitions and provide valued suggestions for reports on race-based discrimination in India. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2023. -
Perceived Discrimination of Old Settlers in Sikkim
The old settlers in Sikkim are a community of mainland Indians whose ancestors had settled at least 15 years before the merger with India in 1975. At present, the total population of the community is less than three thousand individuals, comprising various ethnicities. This qualitative study focuses on the perceived discrimination of the old settlers, who form a demographic minority in the state. Data was collected using telephonic interviews from a sample of 11 old settlers. Thematic analysis indicated racial differences between the northeasterner indigenous community and mainland Indian old settlers as a major reason for perceived discrimination. The participants expressed the experience of negative emotional reactions, such as anger and disappointment, when they faced discrimination. The participants also felt betrayed by the government of India because they did not receive adequate protection for their rights when their identity in Sikkim changed from foreigners to citizens. Reactions to discrimination included migrating out of the state, experiencing negative emotions such as anger, disappointment and fear, and learned helplessness. 2022 Bhasker Malu, Santhosh Kareepadath Rajan, Nikhita Jindal, Aishwarya Thakur, Tanvi Raghuram. -
A Systematic Review on Prognosis of Autism Using Machine Learning Techniques
Quality of life (QoL) and QoL predictors have become crucial in the pandemic. Neurological anomalies are at the highest level of QoL threats. Autism is a multisystem disorder that causes behavioural, neurological, cognitive, and physical differences. Recent studies state that neurological disorders can result in dysfunction of the brain or whole nervous system which may cause other symptoms of Autism. The paper focuses on reviewing various Machine Learning techniques used for diagnosing Autism at an early age with the help of multiple datasets. The study of brain Magnetic Resonance Imaging (MRI) provides astute knowledge of brain structure that helps to study any minor to significant changes inside the brain that have emerged due to the disorder. Early diagnosis leads to a healthy life by getting timely treatment and training. "Early diagnosis of autism spectrum disorder" is an objective and one of the prime goals of health establishments worldwide. The research paper aims to systematically review and find which machine learning algorithms are efficient for the prognosis of autism. The Electrochemical Society -
A comprehensive investigation on machine learning techniques for diagnosis of down syndrome
Down Syndrome is a chromosomal disease which causes many physical and cognitive disabilities. Down Syndrome patients are more vulnerable than any other patient. Medical experts started knowing it now with keen awareness. In recent years it has become a field of interest for many researchers, medical experts and social organisation. For the researchers it is an area of interest where very little work is done and a lot to be explored. Machine Learning consists of different processing levels like pre-processing, segmentation, feature selection and classification. Each level contains a vast set of techniques like filters, segmentation algorithms and classifiers. Machine Learning is one of the most popular algorithm, which is used to automate the decision making process with higher rate of accuracy in less time with least error rate. Machine Learning proved its significance with highest rate of accuracy in decision making and problem solving in almost all the fields but automated decision making in medical science is still a challenge. This paper reviews the different works done in the field of Down Syndrome using Machine Learning applied on different medical images, and the techniques like pre-processing, segmentation, feature selection and classification. The aim of this research work is to analyse and identify the Machine Learning methodologies that works efficiently to detect Down Sundrome. 2017 IEEE. -
AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages
High-dimensional data, lower detection accuracy, susceptibility to manual errors, and the requirement of clinical experts are some drawbacks of conventional classification models available for Autism Spectrum Disorder (ASD) detection. To address these challenges and explore the affiliated information from advanced imaging modalities such as Magnetic Resonance Imaging (MRI) in structural MRI (sMRI) and resting state-functional MRI (rs-fMRI), the study applied an Artificial Intelligence (AI) approach. In this context, AI is used to automate the feature extraction process, which is crucial in the interpretation of medical images for diagnosis. The work aims to apply AI-based techniques to extract the features and identify the impact of each feature in the Autism diagnosis. The morphometric features were extracted using sMRI images and rs-fMRI scans were employed to fetch functional connectivity features. Surface-based, region-based, and seed-based analyses are performed for the whole brain, followed by feature selection techniques such as Recursive Feature Elimination (RFE) with correlation, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and graph theory are implemented to extract and distinguish features. The effectiveness of the extracted features was measured as classification accuracy. Support Vector Machine (SVM) with RFE is the best classification model, with 88.67% accuracy for high-dimensional data. SVM is a supervised learning model that outperforms other classification models due to its capability to handle high-dimensional data with a larger feature set. Medical imaging modalities provide detailed insights and visual differences related to various cognitive conditions that must be recognized accurately for efficient diagnosis. The study presented an empirical analysis of various Feature extraction approaches and the significance of the extracted features in high-dimensional data scenarios for Autism classification. 2024 Meenakshi Malviya Chandra J and Nagendra N. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Role of Artificial Intelligence in Neuroimaging for Cognitive Research
Artificial intelligence (AI)-based solutions are used in most of our daily activities. AI has been adapted and it has found various applications. Cognitive research is one area where AI has been applied to understand the hidden patterns in the data. Neuroimaging techniques investigate the neural basis of cognitive processes like perception, attention, memory, language, reasoning, decision-making, and problem-solving. The irregularities in the cognitive process lead to cognitive disabilities and diseases. Neuroimaging techniques, including magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), and positron emission tomography (PET), along with other data-gathering techniques, are studied to identify cognitive disorders. The imaging techniques generate large amounts of complex data. AI methods, including machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision, are applied and used to analyse and interpret the data generated by various imagining techniques. Numerous techniques have been designed, developed, and proposed to handle the neuroimaging data for cognitive research with the help of AI techniques. AI techniques include ML algorithms like decision trees, random forest, support vector machine (SVM), principal component analysis (PCA), and DL algorithms, including convolution neural networks (CNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Recent advancements in the field of neuroimages use AI techniques to preprocess, process, and analyse the data generated by various neuroimaging modalities. This chapter provides an in-depth analysis and summary of various AI techniques for processing neuroimages for cognitive disorders. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors. -
Bacterial biofilm inhibition activity of ethanolic extract of hemidesmus indicus
Multi-drug resistance is one of the biggest nightmares in the field of healthcare today. Adding on to this, some bacteria like Staphylococcus aureus and Pseudomonas aeruginosa have the ability to form biofilms. These essentially are large colonies of bacteria that are held together by polysaccharides and other biomolecules which in turn facilitate in their adherence to solid substrate both natural and synthetic. This further creates a life-threatening implication leading to nosocomial infections like pneumonia, Urinary tract infections (UTI), etc. increasing the co-morbidities and mortality of critically-ill patients. The combination of antimicrobial resistance, ability to form biofilms and threat of nosocomial infections calls for a need to investigate newer, safer alternatives. Plant based medicaments have been used for centuries and they are a great alternative to synthetic drugs. In the present study, ethanolic extracts of Hemidesmus indicus was evaluated against clinically-important multi-drug resistant organisms. Percentage biofilm inhibition of plant extracts of Hemidesmus indicus by crystal violet assay method. Triplicate analysis was done and data obtained was statistically interpreted using Microsoft Excel. Alcoholic extracts of Hemidesmus indicus exhibited significant biofilm inhibitory activity against the common bacteria Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Bacillus subtilis. Further, isolation of the chief active constituent responsible for Anti-biofilm activity is in process. 2020, National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved. -
Influence of employees' perception on the use of flexible work arrangements
The study aims to explore the factors that influence the perception of employees on the usability of flexible work arrangements and to predict whether those factors induce them to opt for such flexible practices. The data was collected from 239 Indian employees working across different sectors of the country. The study employed a quantitative approach for data collection by using a structured questionnaire consisting of close-ended questions. The data was analyzed using factor analysis, binomial logistic regression and Analysis of Variance on SPSS Statistics 25. The study identified five major factors that influenced the employees perception about using flexible work options. Among them two factors namely, FWA perquisites and FWA anxiety were found significant in predicting the employees use of flexible work options. Further, it was found that married employees recognized strong benefits from using flexible options. This study contributes to the existing literature by unveiling the mindset of Indian employees towards flexible work arrangement and suggests that the employers, society and the government should create favorable environment for deploying flexible work practices. 2020 IJSTR. -
Fire Safety Challenges in Electrical Shafts: A Case of Fire Accident at High Rise Residential Building in Bengaluru, India
Fire safety in high-rise residential buildings is a complex issue, especially when it comes to electrical shaft fires. These enclosed vertical shafts help the rapid spread of smoke, toxic gases and heat across different floors and pose a high risk to the occupants and affect the evacuation process. However, the actual life fire outbreaks show that the existing measures are still wanting as far as evacuation safety is concerned. These issues are addressed in this research through the use of performance-based fire design (PBFD) to assess fire behaviour and evacuation patterns in high-rise buildings. The study then simulates using PyroSim and Pathfinder simulation software, the visibility of smoke, reduction of visibility, CO and CO2 levels, temperature changes, and congestion of the evacuation corridors. Unlike other studies, this paper combines both fire development and occupants response to give a holistic approach to the issue of evacuation challenges. One of the findings of this research is the analysis of the ASET and RSET whereby it was found that the current provisions in fire safety do not ensure a safe exit. It was established that RSET is much higher than ASET, which points to a severe lack in current fire safety solutions. Simulations incorporating NBC-2016 (National Building Code-2016) provisionssuch as fire-rated doors, sprinklers, and mechanical ventilationdemonstrate improved outcomes, reducing RSET from 1,272 seconds to 746 seconds. However, persistent challenges such as congestion, bottlenecks, and hazardous gas levels highlight the need for enhanced fire safety strategies. This research offers practical recommendations to improve evacuation effectiveness through better ventilation, compartmentalization, and advanced suppression systems. The findings contribute to risk mitigation strategies, expand the knowledge base for fire safety, and provide a foundation for improving fire safety regulations in high-rise buildings. 2025 by authors, all rights reserved. -
Demographic Determinants of Fire-Safety Behavior in High-Rise Residential Buildings: A Survey-Based Behavioral Analysis from Bengaluru, India
This study explores the role of demographic and experience-based parameters for fire safety behavior among residents of high-rise residential apartment buildings in the city of Bengaluru, a metropolitan capital in India. Data were gathered through a questionnaire-based survey among 262 residents. Multiple regression analysis was used to assess the correlation among demographic parameters and behavioral responses during evacuation. The results show that age (R2 = 0.154, p = 0.004), presence of vulnerable household members (R2 = 0.137, p = 0.022), and prior fire experience (R2 = 0.157, p = 0.004) are statistically significant predictors of fire-safety behavior. In contrast, gender (R2 = 0.117, p = 0.073), educational qualifications (R2 = 0.109, p = 0.136), and chronic health conditions (R2 = 0.121, p = 0.500) do not exhibit significant associations. Cross-tabulation analysis further indicates that residents who have received fire-safety training prioritize immediate evacuation, whereas untrained residents display delay behaviors. By providing empirical behavioral evidence from an Indian metropolitan context, this study highlights the demographic heterogeneity in evacuation behavior and supports the integration of behavioral realism into performance-based fire safety design for high-rise residential buildings. (2026), (Dr D. Pylarinos). All rights reserved. -
Personal fableness and perception of risk behaviors among adolescents
Adolescence is a crucial period where one tends to identify who they are as an individual. However, as a teenager is struggling to find his/her place in this world, it is also a time where they are prone to engaging in risk behaviors, which tend to have an extreme psychological impact. The objective was to explore the experiences of an adolescent who engages in risk behaviors and to understand their level of personal fables. The study was a qualitative design with content analysis with semi-structured interviews of ten male adolescents aged 16-18 years. The major findings of the study indicated that adolescents pattern of thinking revolves around the fact that they are invincible and invulnerable. Furthermore, adolescents are aware of the risks they are putting themselves through and how in the process they are hurting others. The implications of the study are to conduct more life skill programs in schools; greater awareness has to be created on the impact and harmful effects of such behaviors. 2018, Indian Journal of Public Health Research and Development. All rights reserved. -
THE MEDIATING EFFECT OF HEEDFUL INTERRELATING ON SELF DETERMINATION AND THRIVING AT WORK AMONG UNIVERSITY FACULTY MEMBERS; [EL EFECTO MEDIADOR DE LA INTERRELACI ATENTA EN LA AUTODETERMINACI Y EL PROSPERAR EN EL TRABAJO ENTRE LOS MIEMBROS DEL PROFESORADO UNIVERSITARIO]; [O EFEITO MEDIADOR DA INTER-RELAO CUIDADA NA AUTODETERMINAO E NO PROSPERO NO TRABALHO ENTRE MEMBROS DO FACULDADE UNIVERSITIA]
Objective: The objective of this study is to empirically examine the mediating effect of heedful interrelating on the direct effect of self-determination and thriving at work among university faculty members. Theoretical Framework: The organismic human integration philosophy forms the theoretical underpinning for the study. The conceptual model is built by integrating self-determination theory (SDT) with the theory of heedful interrelating. Method: Following an explanatory research design, data from 396 university faculty members PAN India was used to test the conceptual model with the PLS-SEM bootstrapping technique. Results and Discussion: The findings validate a significant direct influence of self-determination on thriving at work. Furthermore, there exists a significant mediation effect of heedful interrelating between self-determination and thriving at work. Through causal mediation, it is interpreted that self-determined and autonomously motivated behaviors, stemming from the satisfaction of universal basic psychological needs of autonomy, competence, and relatedness, play a pivotal role in fostering heed-based behavior within an individual. Research Implications: This empirical study validated the organismic integration theory of human nature in the academic sector through the positive direct effect. Implications for the sample of university faculty members suggest the use of heedful interrelating during group tasks through the dimensions of contributing, representing, and sub-ordinating. Originality/Value: This study makes significant original theoretical contributions to the SDT literature and to the SDT puzzle, firstly, by adding heed as a novel indicator to self-determination theorys relatedness dimension and secondly, by validating the role of heedful interrelating in bridging the dialectic gap within the self-determination theory. 2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved. -
Can Heedful Interrelating Be a Self-empowering Approach to Thwart Maladaptive Workplace Functioning? An Integrative Literature Review
Maladaptive workplace functioning hinders task completion, work-goal attainment and collaborative interactions, thereby affecting the optimal utilisation of human capital, learning outcomes and organisational sustainability. By integrating self-determination theory, the theory of heedful interrelating and the socially embedded model of thriving at work, this study proposes a conceptual model as a self-empowering approach to thwart maladaptive functioning. A five-stage integrative literature review was conducted to examine the available knowledge base, critically review and synthesise selected literature on heedful interrelating to locate a knowledge gap and bring forth a new way of thinking to address employee workplace maladaptive functioning. A sample of ten empirical articles was consolidated to gauge the antecedents and outcomes of heedful interrelating. The originality of the study lies in bridging the dialectic gap of self-determination theory, introducing heed as a novel factor to the relatedness dimension and employing an intelligent review methodology to propose a practical workplace solution to maladaptive functioning. The Author(s) 2025. -
A Study on Enhancing E-Governance Applications Through Semantic Web Technologies
International Journal of Web Technology, Vol-1 (2), pp. 53-59. ISSN-2278-2389 -
Impact of Goal Divergence, Unbalanced Dependence and Miscommunication on Marketing Channel Satisfaction
Purpose: Marketing channel satisfaction is a critical factor influencing the efficiency and long-term sustainability of distribution networks. However, conflicts arising from goal divergence, unbalanced dependence, and miscommunication often disrupt channel relationships, affecting overall satisfaction levels. This study examines the impact of these three conflict-inducing factors on marketing channel satisfaction, drawing insights from empirical research conducted in the fast-moving consumer goods (FMCG) sector. FMCG sector is considered as the barometer of any economy because of its wide reach to both the urban and rural market. Research design, data and methodology: Using a structured survey and statistical analysis, the study identifies the extent to which goal misalignment, power imbalances, and communication breakdowns contribute to dissatisfaction among channel members. Results: The findings highlight that goal divergence leads to reduced cooperation, unbalanced dependence fosters opportunistic behaviour, and miscommunication exacerbates misunderstandings, collectively diminishing channel satisfaction. The study contributes to the literature on channel conflict management and offers practical implications for businesses seeking to enhance collaboration, trust, and efficiency in their marketing channels. Conclusions: The study explores how the marketing channel members like distributors, wholesalers and retailers can reduce distribution channel conflict and enhance marketing/distribution channel satisfaction. This is still important even though online selling and e-commerce has become the order of the day. This study is very relevant in the field of distribution science. The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://Creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.