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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. -
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) -
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. -
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 -
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. -
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. -
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 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. -
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. -
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. -
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. -
Amberlite-15 promoted an unprecedented aza Michael rearrangement for one pot synthesis of dihydroquinazolinone compounds
A new one pot multicomponent annulation strategy for the synthesis of various dihydroquinazolinone compounds has been developed using Amberlite-15 as a catalyst, giving good to moderate yields. In this reaction the substrate scope for amines and aldehydes was also investigated. The reaction has been checked on a large scale and the possible reaction mechanism has also been proposed. The Royal Society of Chemistry 2018. -
Ambient monitoring in smart home for independent living
Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes. Springer Nature Singapore Pte Ltd. 2019. -
Amide-enriched pod-based carbon nanospheres for enhancing supercapacitor performance: A value-added approach for solid state supercapacitors
The present work involves the fabrication of symmetric solid-state supercapacitors (SSSCs) using amide-functionalized carbon nanospheres (CNS) derived from Magnolia champaca pods, a bio-waste material. The pods were carbonized at temperatures ranging from 400 C to 1000 C, with CNS at 800 C (MC800) showing best electrochemical performance. The synthesized materials, i.e., MC400, MC600, MC800, MC1000, were characterized by techniques such as FESEM, HR-TEM, FTIR, XRD, Raman spectroscopy, and BET. Amide functionalization, achieved through the use of 2,3,4-trifluoroaniline (TFA), enhanced charge storage capacity by improving ion transport and surface interaction, resulting in the functionalized CNS labeled as MC800/COOH-TFA. The electrochemical investigation of the CNS was studied via techniques such as cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS). The functionalization led to two-fold increase in specific capacitance from 243 Fg?1 to 410 Fg?1 at a current density of 0.25Ag?1 in 3 M KOH. The SSSCs was fabricated using MC800/COOH-TFA with a PVA-KOH gel electrolyte demonstrating a good areal capacitance of 40 mFcm?2 at 1.0 mAcm?2. Moreover, the device exhibited excellent energy density of 5.54 ?Whcm?2 and cycle stability, retaining 71.75 % of its capacitance after 10,000 charge-discharge cycles. The response time of the functionalized sample has been reduced to 2.31 s (MC800/COOH-TFA) from 4.73 s (MC800). These results highlight the potential of amide functionalized CNS in producing efficient, sustainable energy storage devices with improved performance. 2025 Elsevier Ltd -
Amine functionalized carbon quantum dots from paper precursors for selective binding and fluorescent labelling applications
We report a novel synthesis route for preparing carbon quantum dots (CQDs) of customized surface functionality from readily available precursors. The synthetic strategy is based on the chemical modification of paper precursors prior to preparing CQDs from them. The pre-synthesis modification of paper precursors with (3-Aminopropyl) triethoxy silane (APTES) enabled us to synthesize CQDs with amine functional groups on the surface. The silane coupling via condensation between the ethoxy group of APTES and the cellulose hydroxyl group on the paper resulted in the tethering of amine groups on the paper substrates, which are retained as surface-bound species during the synthesis of CQDs from the modified paper. Amine functionalization on the surface of CQDs helped us use them in applications such as DNA binding. We analyzed the interaction of CQDs with calf thymus DNA (CT-DNA), and the results imply their propensity as an efficient biological probe. The synthetic strategy presented here can also be extended to other functional groups. 2022 Elsevier Inc. -
Amine-functionalized MIL-101(Fe)-NH2@ZIF-8 composite for efficient adsorption of Pb2+ ions
Heavy metal contamination of water resources poses a serious environmental and public health threat, necessitating the development of efficient and selective adsorbent materials. In this study, a hierarchical MIL-101(Fe)-NH2@ZIF-8 composite was successfully fabricated via an interfacial growth strategy, integrating amine-functionalized MIL-101(Fe)-NH2 and ZIF-8 to achieve a synergistic micro-mesoporous architecture with accessible functional sites. The composite was thoroughly characterized by FTIR, PXRD, TGA, BET, and SEM-EDX analyses, with elemental mapping confirming the structural integration and resulting in enhanced porosity, thermal stability, and functional group availability. The material exhibited a remarkable Pb2+ adsorption efficiency of 94.9 % and a maximum adsorption capacity of 297 mg/g, significantly superior to the adsorption of other metal ions (Cd2+, Cu2+, Ni2+, and Cr2+). Atomic absorption spectroscopy (AAS) validated the exceptional selectivity of MIL-101(Fe)-NH2@ZIF-8 for Pb2+ ions. The enhanced performance is attributed to the synergistic effect of accessible amine (?NH2) functionalities, Fe?O coordination sites, and hierarchical porosity enabling strong metal binding and rapid diffusion. These findings highlight the exceptional potential of MIL-101(Fe)-NH2@ZIF-8 as an advanced adsorbent for Pb2+ removal from water, offering a practical pathway to address critical environmental challenges and promote sustainable human health and ecological protection. 2025 Elsevier B.V. -
Amorphous Ru-Pi nanoclusters coated on polypyrrole modified carbon fiber paper for non-enzymatic electrochemical determination of cholesterol
A facile electrochemical sensor based on Ruthenium-Phosphate (Ru-Pi) was developed by electrodeposition of Ru-Pi on Polypyrrole (PPy) modified carbon fiber paper (CFP) electrode. Phosphate buffer solutions of neutral pH containing RuCl3 was used for voltammetric deposition of Ru-Pi on PPy/CFP electrode. The modified electrodes were characterized by High resolution transmission electron microscopy (HRTEM), High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), Field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and electroanalytical techniques. A significant amount of phosphate (Pi) and Ru centers in higher oxidation states were present on Ru-Pi film deposited on PPy/CFP substrate. Pi plays a significant role during catalyst deposition and in its activity toward determination of Cholesterol. DPV studies demonstrated that under optimum conditions, the developed sensor has displayed a wide linear dynamic range between 0.16 nM and 20.0 nM with a superior detection limit of 0.54 10-10 M. The proposed method was effectively applied in the nonenzymatic determination of cholesterol at an ultralow level in human blood serum samples. The method displayed high selectivity toward cholesterol in the presence of other interfering substances. 2019 The Electrochemical Society. -
Amorphous Ru-Pi nanoclusters decorated on PEDOT modified carbon fibre paper as a highly efficient electrocatalyst for oxygen evolution reaction
Amorphous Ru-Pi nanoclusters deposited on PEDOT modified carbon fibre paper electrode have been investigated as a potential oxygen evolution electrocatalyst. CFP/PEDOT/Ru-Pi electrode was prepared by electrodeposition of Ru-Pi nanoclusters on PEDOT decorated CFP using cyclic voltammetry (CV). Field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy (FESEM-EDS), attenuated total reflection with Fourier-transform infrared spectroscopy (ATR-FTIR) and X-ray diffraction (XRD) were used for physicochemical characterization. Linear sweep voltammetric (LSV) studies corroborated that CFP/PEDOT/Ru-Pi has exhibited higher oxidation peak current when compared to other modified electrodes. CFP/PEDOT/Ru-Pi electrode has displayed better catalytic activity towards oxygen evolution reaction at low onset and over potential. The modified electrode has also offered better stability towards the oxidation reaction in phosphate buffer solution (PBS) and the working stability of these electrodes were determined using LSV and CV. 2021 Elsevier B.V. -
Amorphous versus crystalline Al2O3nanoparticles: A comparative study in photocatalytic dye degradation
This study focuses on the synthesis of aluminum oxide (Al2O3) nanoparticles and compares their amorphous and crystalline phases, emphasizing their suitability for photocatalytic dye degradation. The as-prepared Al2O3, synthesized using the sol-gel technique, is found to have an amorphous nature, which is later annealed at 1200C to obtain its ? phase of crystalline nature. Despite the widespread applications of aluminum oxide in various fields, the differences between its amorphous and crystalline phases are not well understood. This work bridges this gap by evaluating the amorphous and crystalline phases of Al2O3, particularly for dye degradation. As technologies advance to enhance aluminum-containing photocatalytic materials by doping, composites, and hybrids, understanding the impact of material phase on photocatalytic capabilities becomes crucial. The research comprehensively assesses structural, functional, morphological, optical, and dye degradation characteristics. Remarkably, amorphous Al2O3 demonstrates superior dye degradation efficacy compared with its crystalline counterpart, achieving an enhanced degradation efficiency of 87.2% for rhodamine B, a commonly used azo dye in the printing and textile industries. 2024 Emerald Publishing Limited: All rights reserved.