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Amalgamation of IoT, Blockchain, Artificial Intelligence for Metaverse
The metaverse is a set of technologies that uses a computer to create a virtual world of reality and human connections. Some of the most significant enablers of this evolution are the Internet of Things (IoT), blockchain, and artificial intelligence (AI). These technologies not only provide a better experience but also introduce a new way of how security, efficiency, and inter-activeness should be done within the Metaverse. This chapter discusses the intersection between IoT, blockchain, and AI and its relevance in the framework of Metaverse. It discusses how IoT devices generate environments, the blockchain maintains high levels of security and provides digital ownership, and AI facilitates interactions. The rise of these technologies guarantees that the use of the virtual worlds will be consistent and also enhance the user experience, yet the blend of these technologies brings a number of difficulties like interoperability problems, data privacy problems, and also the concern of combining such a lot of various systems. The Metaverse has been explored in these challenges to achieve its full potential. The objective of this chapter is to paint a picture of how IoT, blockchain, and AI should be utilized to improve the Metaverse. This chapter presents an analysis of technical and ethical issues, offers potential solutions to the current problems, and outlines the possible directions of further development. Our study thus points out that the application of these technologies together offers an enormous opportunity to propel the development of the Metaverse in its quest to deliver virtual spaces that are secure, intelligent, and interactive. The final part of the chapter outlines the long-term effect on the society as well as the future prospects for development and the potential ethical challenges in this popping field of study. 2025 Scrivener Publishing LLC. -
Amalgamation of corporate social responsibility with the principles of the circular economy for sustainable growth
The next chapter extends more with the discussion of CE principles integrated into CSR strategies maintaining a business case strong in sustainable practice beyond the traditional model. Closed- loop supply chains, sustainability in product design, Product- as- a- Service for resource efficiency, waste minimization, and improvement within the corporate resilience system come to mind as CE- oriented strategies. This chapter continues to cover the challenge of responsible leadership and advocacy in policy to push over hurdles, support sustainable culture change, and position companies as corporate citizens. It brings out attention to future trends of urban mining and CE in developing markets, showing how companies adopting circular models will derive long- term growth and competitiveness. Recommendations to firms to totally embed CE in their CSR framework could include, notably around actionables, especially in terms of strong metrics, engagement, and creating leadership that supports environmental and social accountability. 2025, IGI Global. All rights reserved. -
AMAA-GMM: adaptive Mexican axolotl algorithm based enhanced Gaussian mixture model to segment the cervigram images
Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance. Before segmenting the cervical region, specular reflection removal is an efficient approach. Cervical cancer can be found using a visual check with acetic acid that turns precancerous and cancerous areas white and these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-white areas and should therefore be removed. So, in this paper, specular reflection removal with segmenting the cervix region in a colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican axolotl optimisation (AMAO) algorithm. The performance of the proposed approach is analysed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. Copyright 2026 Inderscience Enterprises Ltd. -
Alzheimer's Disease Detection using Machine Learning: A Review
Alzheimer's is a progressive brain disorder which is an untreatable, and inoperable and mostly affect the elderly people. There is a new case of Alzheimer's disease being discovered globally in every four seconds. The outcome is fatal, as it results in death. Timely identification of Alzheimer's disease can be beneficial for us to get necessary care and possibly even avert brain tissue damage by the time. Effective automated techniques are required for detecting Alzheimer's disease at very early stage. Researchers use a variety of novel approaches to classify Alzheimer's disease. machine learning, an AI branch use probabilistic technique that allow system to acquire knowledge from huge amount of data. In this paper we represent a analysis report of the work which is done by researcher in this field. Research has achieved quite promising prediction accuracies however they were evaluated the the non-existent datasets from various imaging modalities which makes it difficult to make the fair comparison with the other methods comparison among them. In this paper, we conducted a study on the effectiveness of using human brain MRI scans to detect Alzheimer's disease and ended with a future discussion of Alzheimer's research trends. 2021 IEEE. -
Alzheimer's Disease Detection using Deep Feature Extraction and Explainable Machine Learning
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by gradual cognitive decline, posing significant diagnostic challenges that necessitate automated detection systems to aid clinical decision-making. This study presents an explainable machine learning framework for binary dementia classification using deep feature extraction from magnetic resonance imaging. A pretrained ResNet50 convolutional neural network was employed to extract 2048-dimensional feature vectors from 86,437 MRI slices derived from the OASIS1 dataset, encompassing 347 subjects. The dataset was imbalanced, containing 67,222 Non-demented and 19,215 demented slices (combining very mild, mild, and moderate dementia). The aggregated features at the Subject-level were used to train three machine learning classifiers: Logistic Regression, Random Forest, and XGBoost. The XGBoost model achieved the highest accuracy of 77.14, with a precision of 0.84 and a recall of 0.87 for Nondemented cases, demonstrating strong discriminative capability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations highlighted the hippocampus and temporal lobes as key regions influencing predictions, aligning with established Alzheimer's pathology. The study demonstrates the potential of combining deep feature extraction with interpretable machine learning for automated dementia screening. 2026 IEEE. -
Alzheimer's disease : A challenge in the face of modern era /
Mapana Journal of Sciences, Vol.12, Issue 2, pp.19-36, ISSN No: 0975-3303. -
ALVIS(Alter Vision-An expertise system ) /
Patent Number: 202241026683, Applicant: B Ramamurthy. -
ALVIS(Alter Vision-An expertise system ) /
Patent Number: 202241026683, Applicant: B Ramamurthy. -
Alternative Worldview: The Naga Weretiger, an Ecolegend in When the Millet Fields Flower
This paper analyses the Naga Eco-legend tekhumevi to introduce an alternative worldview through Indigenous communities' philosophy and lived experience. In the context of contemporary environmental discourses, literature plays a significant part in highlighting the affective folklore guiding ethical, environmental practices in regions that are considered ecologically rich areas. Foregrounding the vibrant tapestry of North East Indian Indigenous cultures, it aims to discuss the impact of extraordinary stories on the lives of Nagas and how they shape the community's worldviews. This includes their relationships with the non-human world and their cultural identity. The paper also discusses the vitality of the traditional ecological knowledge of the Indigenous communities and its potential to offer alternative ecological sustenance ethics through holistic worldviews. The oral tradition of the Naga community has re-emerged time and again as a potent tool in offering ecological solutions and abiding by the ethics of sustenance and co-existence. The paper discusses an example of such a toolthe Naga weretiger, or tekhumevi's colonial imagery in the Naga oral histories and lore. However, the perception of such philosophical instruments sees a change because of social and ideological shifts that may be attributed to the intervention of scientific technologies, religion, worldviews, and rationale. Similarly, the accelerated climate health crisis has shifted the focus to an inclusive approach in the 'literature of nature', especially towards the more-than-human, as an alternative to this crisis. The paper reinforces the importance of folk literature and its relevance in the contemporary Naga community, reaffirming Indigenous cosmovision and epistemologies as spaces of resistance and representation. Avinuo Kire's "When the Millet Fields Flower" from The Last Light of Glory Days (2021) intersects magic, terror, community, spiritualism, and ecological ethics. The tekhumevi narrative reinstates the Naga ecological wisdom of bridging the gap and promoting a liminal existence/relationship between the Naga people and non-human entities and spirits. 2026, Knowledge Hub Publishing Company Limited (Hong Kong). All rights reserved. -
Alternate models to dark energy
One of the unresolved questions currently in cosmology is that of the non-linear accelerated expansion of the universe. This has been attributed to the so called Dark Energy (DE). The accelerated expansion of the universe is deduced from measurements of Type Ia supernovae. Here we propose alternate models to account for the Type Ia supernovae measurements without invoking dark energy. 2017 COSPAR -
ALT speech recognition system using F0 improvement and spectral tilt method
Human Beings use voice as the medium for communication. Human Speech is a very complex signal with multiple frequencies, amplitudes and intensities that mix up to convey specific information. In international terminology, voice disorders are described as dysphonia. Various dysphonias are clearly organic origin due to nervous, muscular, neuro or cellular degenerative disease affecting the body or it is from local laryngeal changes. Other dysphonias having no visible laryngeal causes are grouped as non organic involving habitual dysphonias that arise from faulty speaking habits or the psycho genic dysphonias that stem from emotional causes. This paper looks at a speech recognition system for disordered speech generated by Physically Disabled people using Artificial Larynx Transducer (ALT) device from the perspective of Speech Signal Processing. From the ALT speech features like formant, pitch and spectral tilt is estimated. For formant frequency estimation RNN technique is used. Before training the system pitch frequency improvement is accomplished. Now the features and homomorphic based coefficients are used for training the system. The same operation is performed during the test phase and compared with the training set. Comparison and decision making is accomplished using distance estimator. BEIESP. -
Alphabet recognition of American sign language:A hand gesture recognition approach using sift algorithm /
International Journal of Artificial Intelligence & Applications Vol.4, No.1, pp.105-115 ISSN No. 0975-900X (O) 0976-2191 (P) -
Alpha-Bit: An Android App for Enhancing Pattern Recognition using CNN and Sequential Deep Learning
This research paper introduces Alpha-Bit, an Android application pioneering Optical Character Recognition (OCR) through cutting-edge deep learning models, including Convolutional Neural Networks (CNNs) and Sequential networks. With a core focus on enhancing educational accessibility and quality, Alpha-Bit specifically targets foundational elements of the English language - alphabets and numbers. Beyond conventional OCR applications, Alpha-Bit distinguishes itself by offering guided instruction and individual progress reports, providing a nuanced and tailored educational experience. Significantly, this work extends beyond technological innovation; Alpha-Bit's potential impact encompasses addressing educational inequalities, contributing to sustainability goals, and advancing the achievement of Sustainable Development Goal 4 (SDG 4). By democratizing education through innovative OCR technologies, Alpha-Bit emerges as a transformative force with the capacity to revolutionize learning experiences, making quality education universally accessible and empowering learners across diverse socio-economic backgrounds. 2024 ITU. -
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Alpha decay favoured isotopes of some superheavy nuclei: Spontaneous fission versus alpha decay
Spontaneous fission and alpha decay are the main decay modes for superheavy nuclei. The superheavy nuclei which have small alpha decay half-life compared to spontaneous fission half-life will survive fission and can be detected in the laboratory through alpha decay. We have studied the alpha decay half-life and spontaneous half-life of some superheavy elements in the atomic range Z = 100-130. Spontaneous fission half-lives of superheavy nuclei have been calculated using the phenomenological formula and the alpha decay half-lives using Viola-Seaborg-Sobiczewski formula (Sobiczewski et al. 1989), semi empirical relation of Brown (1992) and formula based on generalized liquid drop model proposed by Dasgupta-Schubert and Reyes (2007). The results are reported here. -
Alpha Decay Favoured Isotopes of Some Superheavy Nuclei: Spontaneous Fission Versus Alpha Decay
Romanian Journal of Physics, Vol-57 (9-10), pp. 1335-1345. -
Aloe Vera Protein Encapsulated Platinum Nanoparticles for Pain and Inflammation Therapy
Platinum nanoparticles (Pt NPs) possess unique redox activity, chemical stability, and enzyme-mimetic properties, making them promising candidates for anti-inflammatory and analgesic therapies; however, protein-based green encapsulation strategies for such applications remain largely unexplored. In this study, we report the biochemically assisted synthesis of Aloe vera protein-encapsulated platinum nanoparticles (APPt NPs) using sodium borohydride as the reducing agent and freshly extracted Aloe vera proteins as both stabilizing and capping agents. The formation of Pt NPs was confirmed by a characteristic colour change and validated through physicochemical characterization. X-ray diffraction revealed a face-centred cubic crystalline structure, UVvisible spectroscopy showed absorption in the 220280 nm range due to surface plasmon resonance, and FTIR analysis confirmed the presence of protein functional groups on the nanoparticle surface. APPt NPs demonstrated significant biological efficacy. In vitro assays showed inhibition of protein denaturation (IC50 = 44.52 g/mL) and strong free-radical scavenging activity (IC50 = 10.39 g/mL). In vivo studies revealed ?60% analgesic efficacy at 50 mg kg?1 in the hot-plate model and ?50% inhibition of carrageenan-induced paw edema after 4 h, outperforming the standard drug. These results collectively highlight APPt NPs as a biocompatible, redox-active nanotherapeutic candidate for managing pain and inflammation. 2026 Wiley-VCH GmbH. -
Allowing foreign universities in India: Pros and cons
The UGC has invited responses to the draft guidelines. An honest deconstruction of it must reveal both the advantages and the disadvantages -
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.