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Integrating Artificial Intelligence in Education: Insights From a Teacher Training Workshop
This chapter explores the impact of an in-person AI training workshop on Chilean in-service teachers across a network of four schools. Through a mixed-methods approach-including pre-and post-surveys with qualitative and quantitative data-the study examines shifts in teacher attitudes, knowledge, and intentions to use AI in educational practice. Results show increased confidence, pedagogical alignment, and ethical awareness, particularly regarding inclusion and differentiated instruction. The chapter also highlights the importance of contextualized training, gender representation, and long-term support to ensure equitable and meaningful AI integration in Latin American classrooms. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A global perspective on psychologists' and their organizations' response to a world crisis; [Una perspectiva global sobre la respuesta de los psicogos y sus organizaciones a una crisis mundial]
Around the world, individual psychologists have stepped up to deliver essential services to address the social and emotional sequelae of the COVID-19 pandemic. Many psychological organizations have also responded to this public health crisis, though their efforts may be less widely recognized. Psychological organizations engaged in preventive and mitigation efforts targeted, among others, the general public, local communities, and high-risk groups such as health care providers. They disseminated mental health information to the general public, trained laypersons to provide psychological first aid, and used research to design and evaluate public health responses to the pandemic. In some countries, psychological organizations contributed to the design and implementation of public health policies and practices. The nature of these involvements changed throughout the pandemic and evolved from reactive to proactive, from local to international. Several qualities appear key to the value, impact, and success of these efforts. These include organizational agility and adaptability, the ability to overcome their political inertia and manage conflict, recognizing the need to address cultural differences, and allocating limited resources to high-risk and resource-depleted constituencies where it was needed most. 2021, Sociedad Interamericana de Psicologia. All rights reserved. -
Biofabricated textiles The future of sustainable fashion
[No abstract available] -
AI-Powered Transformation in Home Textiles: Efficiency, Sustainability, and Consumer Experience
Background The home textile sector, including bed linens, towels, and curtains, is under pressure from rising consumer expectations, stricter sustainability standards, and supply chain uncertainties. Artificial intelligence (AI) is emerging as a strategic enabler, offering innovative solutions across design, production, quality control, logistics, and customer interaction. Methods The scope includes a scoping review (20152025) of peer-reviewed literature and reputable industry reports, supplemented by documented corporate cases in home textiles. Inclusion required explicit metrics (e.g., yield %, energy or water usage, forecast error) or reproducible descriptions of AI workflows. Results Analysis shows that AI improves efficiency and competitiveness through multiple pathways: (i) trend forecasting and generative design tools; (ii) optimized color matching and dyeing via machine learning and spectral systems; (iii) automated defect detection and predictive maintenance using computer vision and IoT; (iv) cutting-room efficiency through AI nesting algorithms; (v) supply chain resilience with demand sensing and drone-assisted inventory checks; and (vi) blockchain-based platforms that ensure cotton traceability. On the consumer side, AI enhances personalization and supports the growth of smart bedding products. These applications reduce waste, improve product quality, and reinforce sustainability initiatives. Conclusion AI complements rather than replaces human creativity and craftsmanship. Organizations in the home textile industry that embrace AI strategically across design studios, mill operations, and retail channels can achieve measurable improvements in productivity, sustainability, and consumer trust, positioning themselves for long-term competitive advantage. 2025, Textile Association (India). All rights reserved. -
Laminating techniques for luxury textiles
In this paper, we delved into the transformative aspects of lamination techniques in luxury textiles. We started by explaining luxury textiles and what makes them premium: craftsmanship, exclusivity, superior ingredients, silk, cashmere, fine wool etc. They presented the lamination procedures that are considered pre-processing steps to improve the functionality, lifespan, and appearance of such textiles. -
Mapping the landscape of tourism cities research: a bibliometric analysis of the International Journal of Tourism Cities
Purpose: This research addresses the pressing need for comprehensive studies in the rapidly evolving field of city tourism. This study aims to understand the overall performance of the International Journal of Tourism Cities (IJTC), the structure of knowledge in city tourism research and the prevalent themes and trends arising from IJTC. Design/methodology/approach: A bibliometric analysis was conducted to scrutinize the publication patterns in IJTC. This involved examining parameters such as the annual count of published articles, the keywords used in them and their respective authors. Findings: The findings reveal that IJTC has a growing and diverse publication output, establishing itself as a reputable and influential publication within urban tourism research. The results reflect various aspects and themes in city tourism research. Research limitations/implications: The study has certain limitations. The data used for analysis was obtained exclusively from the Scopus database. The analysis was conducted using only one software package, Bibliometrix. Other software packages may offer different features for bibliometric analysis. The study relied exclusively on quantitative methods for data analysis. Qualitative methods could have provided more nuanced interpretations of the data. Practical implications: Comparative analyses could be conducted between IJTC and other journals within urban tourism or related disciplines. Such research would yield valuable insights into the current state of the field and aid in identifying areas warranting further investigation. Social implications: The findings from this study can inform the decisions and actions of various stakeholders involved in urban tourism. Practitioners and policymakers can draw from this research to make informed decisions. Existing and emerging authors can identify relevant topics for their research. Readers can access pertinent information for their needs. Originality/value: This study offers a unique contribution by thoroughly comprehending the performance of IJTC between 2015 and 2023. It progresses the existing body of knowledge on city tourism research by examining its current status and future trends. 2023, International Tourism Studies Association. -
COVID-19 pandemic and preparedness of teachers for online synchronous classes
COVID-19 pandemic has forced educational institutes to shut down, and teachers are compelled to adopt technology ardently so that the teaching-learning process does not suffer. Gradually, it is being realised that synchronous online classes are required to enhance the teaching-learning experience. The major challenge in India is the lack of preparedness of the teachers, as most teachers have little experience with technology. Nevertheless, they have to adapt themselves quickly. However, to effectively use technology for synchronous online teaching, teachers have to be technology ready and proficient with utilising the platform used for online classes. This study attempts to understand the impact of teachers preparedness on the use of online platforms for synchronous teaching during the COVID-19 pandemic. This paper integrates the technology readiness index (TRI) and technology acceptance model (TAM), also known as the TR and acceptance model (TRAM), to investigate the phenomenon mentioned above. Copyright 2022 Inderscience Enterprises Ltd. -
Sustainable luxury tourism: Promises and perils
Recent decades have witnessed a rising concern regarding the prosperity of the environment and utilisation of resources. A sustainable approach is being promoted in all sectors. In the field of tourism, sustainable tourism is widely discussed among researchers and practitioners. On the other hand, luxury tourism is criticised for lavish resource utilisation to serve the few luxury tourists. There is a need to include sustainability in luxury tourism to benefit the environment, local communities, tourist destination and luxury tourists. However, sustainable luxury tourism is an emerging concept and needs more investigation. This chapter attempts to present the existing knowledge about sustainable luxury tourism by implementing a systematic literature review. Further, the opportunities and challenges associated with sustainable luxury tourism are being highlighted. This study has identified the factors that need to be considered to promote sustainable luxury tourism. Moreover, suggestions of the researchers are being presented to serve as guidelines. This study includes an example of the Diphlu river lodge, which has practised sustainable luxury tourism for many years. The viewpoint of luxury tourists are being understood by analysing the reviews of tourists from TripAdvisor using NVIVO-12 qualitative data analysis software. The combination of literature review and practical information provides insight into sustainable luxury tourism. 2022 by Emerald Publishing Limited. All rights reserved. -
Insights into Artificial Neural Network techniques, and its Application in Steganography
Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions. 2021 Institute of Physics Publishing. All rights reserved. -
Advances in text steganography theory and research: A critical review and gaps
There is an immense advancement in science and technology, and computing systems with the highest degree of security are the present hot topic; however, the domination of hackers and espionage in terms of disclosing the sensitive information are steadily increasing. This chapter presents a theoretical view and critical examination of the few text steganography methods in the contemporary world. It tells the direction in which research has developed over the past few years. Cryptography, the encipherment to a certain extent, protects the data by making it unreadable but not safe. Improvisation of the same can be done using another layer of protection that is steganography in which the secret embedded inside the cover text will not be revealed. 2021, IGI Global. -
Social Media and Steganography: Use, Risks and Current Status
Steganography or data hiding is used to protect the privacy of information in the transit; it has been observed that the information that flows through Online Social Networks (OSN) is very much unsafe. Therefore, people hesitate to communicate their sensitive data on social media.. Most of the information on the online social network is not useful to users and appears to disregard such details. People's actions provided a possibility for digital Steganography through the Internet.. TCPIP covert channels were used for steganography until the last decade. People began to utilize social media as a covert conduit to communicate hidden messages to targeted users as social media grew in popularity. There are numerous Online Social Networks accessible nowadays, ranging from Facebook to the more contemporary Twitter and Instagram. All of them may be utilized as covert channels without the general public noticing. The primary characteristic of steganography is the protection of information privacy; nonetheless, it has been utilized more for illicit message transmission, which is a source of concern. To make matters worse, adversaries are using steganalysis techniques to mess with the concealed data. In this article, we examine the different social media steganography techniques, such as those used on Facebook, WhatsApp, and Twitter, as well as the difficulties that these approaches raise. The positive and negative consequences of social media, as well as its current state, are discussed in this study. This paper discusses how the performances of Steganography methods may be assessed using the Entropy value of the Stego object. A look of the three features of steganography. It has been given with undetectability, robustness, and payload capacity. Finally, the paper's concept's future scope is explored. 2013 IEEE. -
A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks
Increasing prevalence and simplicity of using Artificial Intelligence (AI) techniques, Steganography is shifting from conventional model building to AI model building. AI enables computers to learn from their mistakes, adapt to emerging inputs, and carry out human-like activities. Traditional Linguistic Steganographic approaches lack automation, analysis of Cover text and hidden text volume and accuracy. A formal methodology is used in only a few Steganographic approaches. In the vast majority of situations, traditional approaches fail to survive third-party vulnerability. This study looks at evaluation of an AI-based statistical language model for text Steganography. Since the advent of Natural Language Processing (NLP) into the research field, linguistic Steganography has superseded other types of Steganography. This paper proposes the positive aspects of NLP-based Markov chain model for an auto-generative cover text. The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods. Here the RNN model follows Long Short Term Memory (LSTM) neural network. The paper also includes a case study on Artificial Intelligence and Information Security, which discusses history, applications, AI challenges, and how AI can help with security threats and vulnerabilities. The final portion is dedicated to the study's shortcomings, which may be the subject of future research. 2013 IEEE. -
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Emerging Approaches in Digital Content Security: A Review of Blockchain Technology, Image Authentication, and Identity Management Systems
The rapid growth of digital media exchange and communication technologies has intensified the need for robust methods to ensure data integrity and protect intellectual property. This review paper explores various methodologies for safeguarding digital content, with a particular focus on distributed ledger technologies (DLT) and advanced image processing techniques. DLT, employed in blockchain systems, is highlighted for its ability to secure transactions and data in decentralized networks, mitigating the risks associated with centralized systems. In the realm of multimedia forensics, keypoint-based copy-move forgery detection methods, enhanced with density-based clustering and outlier removal algorithms, have shown superior performance in challenging conditions, effectively identifying forgeries even under geometric distortions and postprocessing. Additionally, the integration of speeded-up robust features (SURF) and polar complex exponential transform (PCET) in forgery detection offers resilience against various distortions, ensuring the authenticity of high-brightness regions in images. The paper also examines the evolution of digital identity management, where blockchain-based systems like BZDIMS employ zero-knowledge proof (ZKP) algorithms to enhance privacy and security. Through a comprehensive comparison of existing models, this paper demonstrates the advantages of these advanced technologies in enhancing data integrity, privacy, and the overall reliability of digital content verification systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Secured forensic image analysis by optimised iterative model with random consensus approaches
Future measurements, software, and scalability testing related to cloud performance are required for forensic image scalability (FIS) optimisations and advancements. An advanced iterative reconstruction model and consensus mechanism must be used to quantitatively evaluate image quality in any blockchain framework since this will have a direct impact on the security and usability of the framework. This work addresses these problems by presenting a fast and efficient forgery detection system based on optimal security, feature extraction, and pre-processing. This will render conventional media security and forensic techniques meaningless. In this work, a random sample consensus (RSC) method is proposed for the analysis of FIS. To ensure that the architecture is as strong and secure as possible, the iterative reconstruction model (IRM) is employed. Initially, one may consider channel processing to be a form of database picture pre-processing. One perspective states that the enhanced chicken swarm optimisation (ECSO) algorithm is used to advance the scaling settings to balance invisibility and power. This RSCs threshold setting reduces the number of excluded matches as well as the root mean square error (RMSE). Enhancement of scalability as well as picture reconstruction demonstrate the utility of the proposed technology. The simulation findings on multiple retinal image datasets demonstrate that the proposed method further enhances accuracy matching by 10.56% and rate of progress by 30% on average compared with the RSC-IRM strategy. 2025 Inderscience Enterprises Ltd. -
Block chain-based security and authentication for forensics application using consensus proof of work and zero knowledge protocol
The technique that checks the origin, integrity, Zero-Knowledge authenticity of photographs is known as image authentication. Numerous studies on image authentication have revealed numerous trade-offs between four desirable features, namely robustness, security, flexibility, and efficiency. This study demonstrated a high-security Forensic Image (FI) as well as an authentication mechanism. Initially, the FI considered image registration with features for the Consensus method (CM) to generate blocks on each feature using a hypothesis test-based similarity measure. Because Proof-of-Work (PoW) blockchain technology is widely used, maintaining the Consensus PoW(CPoW) requires a massive amount of computing power. ZKP authentication is a critical cryptographic mechanism that authenticates network nodes without revealing the users identity or any other data given by the user. The blockchain stores the secret information, as well as the hash value of the original FI. This allows for the tracking of all medical pictures exchanged through the proposed blockchain network. The blockchain stores the private information as well as the hash value of the original medical image. The experimental results indicate the utility of the proposed approach with performance measures in contrast to established security analysis methods. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Image Steganography Using Discrete Wavelet Transform and Convolutional NeuralNetwork
The practice of steganography involves concealing messages within another thing, which is referred to as a carrier. Is thus performed in order to build up a covert communication channel in a rather way that any observers whom has access to such a channel will not be able to detect the act of communication itself. In this research, using the process of stenography, a secret text is transferred across a communication channel using an image as a cover. Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) is used in the above process. The encoding and decoding operation is done by using DWT while the preprocessing and training of images is done by CNN. The training and prediction rate of CNN is 72.4 %. 2022 IEEE. -
Software bug in identification and prediction through software are metrics in object oriented protects :
In the software engineering, quality assurance plays an important role. newlineThe quality assurance as an activity, observes the execution of software project to ensure that the behavior of product is in accordance with the expectations. The testing is associated with quality assurance activities. The testing takes a lot of time and an effort of the tester to test the test newlinecases. Even after enough manual or automatic testing, bugs remain uncovered because of lack of time. So, a need arises to focus on this area to save the time and cost of the organizations. The software developer or newlinetester should be aware about the main reasons of software bugs so that they can focus on the right part of the code at the right time. Need of introducing product, process and project metrics is also very essential for newlinethe identification of major causes of bugs. Predictions will always be best if the history of project is taken into consideration. We can come up with accurate predictors with the help of root causes of the software bugs. Several bug prediction models can use bug indicators as the input of model to predict the number of bugs. newlinePrediction attempts to provide quantitative measures to help the software testers and developers. With more number of bug indicators, a step can be taken towards wider horizon of bug prediction thus enabling higher devotion to improve quality of software products. Therefore, identification of several reasons of software bugs and implementation of effective bug prediction models are needed to widen the scope of bug newlineprediction approaches and to improve the software quality. After estimating the future bugs using prediction models, awareness of bug severity is also required to avoid the expected harms to software products. newlineIntroduction of Artificial Neural Network (ANN) was needed to improve the prediction potential. In this work an attempt has been made to associate different levels and types of inheritance through neural network newlineby establishing a correlation framework with diverse types of bug severitie. -
Self-regulating fermentation device /
Patent Number: 202211013682, Applicant: Javin Harpal Singh Kaundal.
A self-regulating fermentation device (100) comprising an outer shell (102) housing a container (104); a lid (106); a sensor (108) configured to sense a temperature of the liquid received within the container (104); a coil (110) configured to heat the liquid placed inside the container (104); a cooling element (112) attached to the lid (106) that is configured to cool down the liquid placed inside the container (104); a controller (114) configured to receive temperature of the liquid from the sensor (108). -
An IOT enabled seats management system of moving bus transportation and method thereof /
Patent Number: 202111041762, Applicant: Mr. Chandan Choubey.
The present invention discloses an IoT Enabled vacant seats management system of moving bus transportation and method thereof. The system and method include, but not limited to, a handheld device with a user interface to view the desired bus on the route available for commuting and checking the vacant seat information in real-time; a GPS unit provided with the handheld device and a communication device, which is installed at bus stand; an infrared sensor to detect further movement and number of the commuters entered the bus in conjunction with a camera device.