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Speech to text conversion and summarization for effective understanding and documentation
Speech, is the most powerful way of communication with which human beings express their thoughts and feelings through different languages. The features of speech differs with each language. However, even while communicating in the same language, the pace and the dialect varies with each person. This creates difficulty in understanding the conveyed message for some people. Sometimes lengthy speeches are also quite difficult to follow due to reasons such as different pronunciation, pace and so on. Speech recognition which is an inter disciplinary field of computational linguistics aids in developing technologies that empowers the recognition and translation of speech into text. Text summarization extracts the utmost important information from a source which is a text and provides the adequate summary of the same. The research work presented in this paper describes an easy and effective method for speech recognition. The speech is converted to the corresponding text and produces summarized text. This has various applications like lecture notes creation, summarizing catalogues for lengthy documents and so on. Extensive experimentation is performed to validate the efficiency of the proposed method. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Trauma, Ontological Exile, and the Trans Self: Reading Transgender Autobiographical Narratives from the Global South
Scholarly understanding of exile usually foregrounds the forced displacement of human beings from one geographical location to another due to war, violence, or fear of persecution. However, exile is also a psychological state of being caused by external factors that need not necessarily be limited to the physicality of dislodgement from a sense of home. This paper explores exile as an ontological condition informed by experiences of trauma, selfhood, and marginalisation from the vantage point of transgender lived experiences from India. The philosophical engagement of these ideas is exemplified by the autobiographical work of a transgender woman, A. Revathi, titled The Truth About Me: A Hijra Life Story. The paper facilitates dialogues between narrativising trauma, psychological exile, and the trans self-using interdisciplinary frameworks of Paul Ilie, Maurice Merleau-Ponty, Edward W. Said, Judith Butler, Cathy Caruth, Sara Ahmed, and Shoshana Felman to examine the interconnection between thoughts on trauma, testimony, inner exile, lived experiences, queer phenomenology, and gender performativity. The study observes that the reclamation of transgender selfhood emerges through the act of self-narration. It also reimagines exile and trauma as philosophical processes of self-awareness and becoming. 2025, University of Western Macedonia. All rights reserved. -
GAN-Based Metaheuristic Techniques for Data Generation and Imbalance Data Control
In cybersecurity today, the power of features such as threat detection, anomaly spotting, and predictive analytics depends heavily on having abundant, properly dispersed datasets. The actual datasets often fall short, suffering both from a lack of volume and skewed class distribution, for example, a flood of routine network activity records overshadowing the infrequent but vital records of malicious behavior. The performance of data-driven models hinges on access to abundant, well-distributed data. However, real-world datasets frequently exhibit inadequate sample sizes and pronounced class imbalances, limiting the viability of complex models. This chapter proposes a novel strategy for generating synthetic data and effectively managing class imbalance, leveraging the integration of Generative Oppositional Networks (GANs) and sophisticated metaheuristic optimization techniques. Rather than settling for fixed GAN architectures, our approach progressively enhances a dynamic GAN framework by deploying a metaheuristic search to identify optimal network topologies, antidote scaling factors, and training schedules. This iterative calibration enables the model to adaptively respond to the imbalance and ensures a richer, balanced synthetic training environment. This adaptive optimization addresses common GAN training pitfalls, mode collapse, and instability while consistently producing synthetic samples that are both precise and varied. What sets the framework apart is its built-in ability to detect and over-represent minority classes, intelligently augmenting the dataset to correct class imbalance without falling back on naive duplication. By equipping GANs with metaheuristic reasoning, the study seeks to elevate data synthesis beyond current limits, generating more robust and impartial machine learning models in any domain where data collection is limited or systematically skewed. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
FEATURE SELECTION AND CLASSIFICATION OF LEUKEMIC CELLS USING IOT AND MACHINE LEARNING
Machine learning and the Internet of Things (IoT) have affected every step of the leukemia process, from diagnosis to understanding to therapy. Consequently, this study delves into the planning of an innovative system that employs IoT and machine learning techniques to precisely differentiate leukemic cells. Depending on the patient's samples, the system uses different ways to feature selection and cell classification. To pick the most informative collection of features that enables stable and accurate cell categorization into suitable categories, the offered research relies on strong machine-learning approaches for feature selection. Next, a classification model is used to classify cells based on their properties using the attributes that have been chosen. There is evidence that the suggested approach can classify leukemic cells with an identification rate of up to 99%, which is greater than the current methods. As a novel strategy for managing massive volumes of biological and medical samples, the suggested method will be an invaluable tool for doctors treating leukemia patients. The system's ability to process data from various Internet of Things (IoT) sources should aid its ability to learn and adapt to real-world clinical settings. With the results of this study in hand, we may be able to detect leukemia sooner, with greater precision, and maybe use more tailored treatments for each patient, leading to better results while reducing healthcare expenditures. 2025, Institute of Mechanics of Continua and Mathematical Sciences. All rights reserved. -
Structural investigation of discrete solvent protonated vanadium and other transition metal complexes of N-[(E)-(3-ethoxy-2-hydroxyphenyl)methylidene]benzohydrazide, synthetic, spectroscopic and cytotoxicity studies
A new ligand 3-ethoxysalicylaldehyde benzoic hydrazone (H2ESB) and its copper(II), nickel(II), cobalt(II), zinc(II), and dioxidovanadium(V) complexes have been synthesized and characterized by elemental analysis, IR, UVVis and EPR studies. Copper(II) complex (2) contains 2,2?-bipyridine as a coligand. Aroyl hydrazone and its copper and vanadium complexes were characterised by single crystal XRD. The vanadium compound crystallized in triclinic space group P1- and copper compound in orthorhombic space group P212121. The solvent molecule DMF protonates to form ammonium ion in vanadium complex which neutralises the charge on the vanadium ion. Both complexes copper and vanadium show distorted square pyramidal geometry. From EPR results, spin Hamiltonian and bonding parameters were calculated. The g values in copper complexes indicate the presence of the unpaired electron in the dx?y orbital. In vitro cytotoxicity studies of aroylhydrazone and its complexes showed that copper, cobalt and vanadium complexes are more cytotoxic than hydrazone and other complexes against Dalton's lymphoma ascites cells (DLA). 2019 Elsevier B.V. -
Novel dioxidomolybdenum complexes containing ONO chelators: Synthesis, physicochemical properties, crystal structures, Hirshfeld surface analysis, DNA binding/cleavage studies, docking, and in vitro cytotoxicity
A series of dioxidomolybdenum (VI) complexes, [MoO2(ESB)H2O]DMF (1), [MoO2(ESB)MeOH] (2), and [MoO2(ESB)H2O]EtOH (3), containing 3-ethoxysalicylaldehyde benzoylhydrazone have been synthesized and analysed using various spectral and analytical techniques such as elemental analyses, IR spectra, UVVis absorption spectra, X-ray crystallography, and Hirshfeld surface analysis. Based on the elemental and spectral analysis, six-coordinate geometry was assigned for these complexes wherein the hydrazone ligand binds to the metal centre in its dianionic enolate form through ONO donor set. Distorted octahedral geometry of complexes 1 and 2 was evidenced from their crystal structures, which is typical for many cis-dioxido complexes of MoVI. The proligand and the new complexes were examined for their DNA binding, DNA cleavage, and cytotoxic properties. The DNA binding efficiency of the compounds in terms of their binding constants (Kb) of the metal complexes was observed to be 1.3727 105 M?1, 3.0194 104 M?1, and 1.13206 104 M?1 for [MoO2(ESB)H2O]DMF (1), [MoO2(ESB)MeOH] (2), and [MoO2(ESB)H2O]EtOH (3), respectively, indicating that these complexes strongly bind to DNA. To determine the binding interactions of the complexes with DNA and protein (BSA), molecular docking studies were carried out. Gel electrophoresis study reveals the fact that the complexes cleaved supercoiled pUC-18 DNA to nicked form (Form II) in the presence and absence of H2O2. The complexes showed significantly high cytotoxicity against MCF-7 (breast cancer cells). 2021 John Wiley & Sons, Ltd. -
An Enhanced Automation Analysis for Structural Algorithm in Agro-Industries Using IoT
The Internet of Things (IoT) based structural algorithm for automatic agriculture refers to the system of using powerful real-time data collected from a variety of sensors with software and analytics to autonomously manage agro-ecosystems. This algorithm can be used to monitor environments, analyze data and use this knowledge to take specific actions to help farmers and producers maximize their production and profitability. This algorithm provides an unprecedented level of precision, accuracy and control over the agricultural environment, allowing greater efficiency and optimization in farming practices. It enables monitoring, scheduling, and control of different agro-ecosystem components, such as water, soil, fertilizer, light, humidity, temperature, soil pH and crop growth. The algorithm can also point to general trends and patterns in the environment, as well as offer timely advice to farmers in response to real-time conditions. The algorithm is also capable of automatically diagnosing and responding to unexpected problems, which can help prevent costly mistakes and excessive waste of water, fertilizer, energy, etc. 2023 by the authors. -
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Examining the effect of explicit instruction on vocabulary learning and on receptive-productive gap: An experimental study
This research study emphasized the importance of explicit instruction and repeated exposure to the target vocabulary for effective reception and production of new words among the second language learners of English. The purpose of the study was to examine the efficacy of the researcher- created supplementary for the prescribed set of vocabulary in the English language textbook. The study aimed to find out the amount of influence the explicit instruction and repeated exposure to the target vocabulary had on the nature of the receptive- productive gap. A total of sixty-two sixth grade students from a Government school in Tamil Nadu, India, participated in the experimental study which was conducted over a period of three months. The results of the study showed that the explicit instruction and repeated exposure to the target vocabulary had a significant amount of influence on vocabulary knowledge when compared to the conventional way of vocabulary instruction. In the conventional mode of vocabulary instruction, reception of vocabulary was found to have an average of 8% influence on the production knowledge, whereas in the case of the experimental group, it amounted up to 72%. This analysis showed that through explicit instruction and repeated exposure to the target vocabulary the receptive- productive gap is significantly reduced. 2020 JLLS and the Authors - Published by JLLS. -
Down syndrome detection using modified ant colony optimization algorithm
Nowadays, the systems related to healthcare are restructured with innovative skills to offer humans more intellectual and proficient healthcare facilities. Various intelligent healthcare systems are exhibited with the help of machine learning and artificial intelligent tools to offer intellectual and expert services. In human body genetic codes are stored in the genes. All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes. In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half. But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome. It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities. This paper introduces an intelligent prediction and classification system for healthcare, feature selection based on density with Ant Colony Optimization (ACO) algorithm for Down syndrome (DS). 2021, Engg Journals Publications. All rights reserved. -
Down syndrome detection using modified adaboost algorithm
In human body genetic codes are stored in the genes. All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes. In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half. But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome. It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for down syndrome prevention. In this work, recognition of Down syndrome utilizes a set of facial expression images. Solid geometric descriptor is employed for extracting the facial features from the image set. An AdaBoost method is practiced to gather the required data sets and for the categorization. The extracted information is then assigned and used to instruct the Neural Network using Backpropagation algorithm. This work recorded that the presented model meets the requirement with 98.67% accuracy. 2021 Institute of Advanced Engineering and Science. All rights reserved. -
Detection of tuberculosis using convolutional neural network with transfer learning
Tuberculosis is sighted as the one of the life causing disease in the recent time. The current research work focus on detection of Tuberculosis using Convolutional Neural Network with Transfer Learning for chest X-ray images. The proposed research work uses two different datasets for detecting Tuberculosis from Chest X-ray images, which is taken from National Institutes of Heaths. During the experimental work, the total sample size used for detecting Tuberculosis is 800 instances. Initially, the image processing techniques were applied to increase the quality of Chest X-ray images. The proposed model uses Convolution Neural Network with transfer learning for the detection of Tuberculosis with 98.7% as accuracy. The proposed model is checked with convolutional neural network without transfer learning. From the experimental evaluation, it is found that the proposed model works better than the Convolution Neural Network without using the transfer learning. 2017, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Covid 19 impact assessment index for manufacturing MSMES /
Patent Number: 202141035478, Applicant: Theresa Nithila Vincent.
The COV1D 19 pandemic disrupted the functioning of enterprises and posed a significant effect on the Micro, Small and Medium Enterprises (MSMEs) sector. The level of impact was varying depending on the type of business. This study aims to develop an index to assess the impact of COVTD 19 on the manufacturing MSMEs by studying the impact on the Key Performance Indicators, namely; Labour, Supply Chain, Production and Revenue. -
Apparel shopping styles of young adult consumers in Bangalore /
Indian Journal Of Marketing, Vol.46, Issue 2, pp.267-279, ISSN No: 0973-8703. -
Online Fake News Detection using Machine Learning and Natural Language Processing Algorithms
Fake news in the digital platforms plays a vital threat to many of the influencing factors of the society. The research focuses on the challenges of online fake news detection from the performance achieved by the machine learning classifiers using the natural processing techniques. Logistic Regression and Random Forest are taken to test the dataset containing labelled fake and real news for the study. The models are evaluated using the key metrices as accuracy, precision, recall, F1-score, confusion matrix and from the ROC AUC score. The research demonstrates which model is more reliable and more consistent for finding the fake and real news. For semantic text analysis BERT embeddings are used in the research as it will help analyze the article with more accuracy and precision. The metrics evaluation and the ROC curve of the both models helps in knowing even the slight deviations projected in the metrics. The differences in the valuations and the metrics values showed the capabilities of both the models in detecting the online news as fake or real. The comparisons made from the two models helps in evaluating the models and to understand the limitations of it as the analyze and detection of the text is more complicated. The research aims to deliver a strong foundation for real-time fake news detection in this new era. 2025 IEEE. -
Managing individual and orgnizational challenges with respect to diversity perceptions and social capital among members of virtual teams,
Diversity encompasses complex differences and similarities in perspectives, identities, and points of view among members of an institution as well as among individuals who make up the wider community. Diversity includes important and interrelated dimensions of human identity such as race, ethnicity, gender, gender identity and newlineexpression, socio-economic status, nationality, citizenship, religion, newlinesexual orientation, ability and age. These differences are important to understand but they cannot be used to predict any individual s values, choices or responses. Organizations with diverse employees are better suited to serve diverse external customers in an increasingly global market. Such organizations have a better understanding of the requirements of the legal, political, social, economic, and cultural environments. Organizations that manage diversity are recipients of more commitment, and better satisfied as well newlineas better performing employees (Patrick and Kumar,2012). newlineEnsuring better social relations among team members has become complex. The nature of teams is not how they used to be, organizations have spread across geographically which has led to the birth of virtual teams. Virtual team members are been separated by time and space this makes it even more difficult to ensure that social capital is being maintained among virtual team members, as only when there is a trust, newlinereciprocity and cooperation among virtual team members they will be better connected individuals who obtain greater advantages, this ensures that groups and organizations improve performance and obtain sustainable competitive advantage (Tsai and Ghoshal, 1998). newlineThe present investigation was focused on understanding the perception of members of virtual teams towards diversity at workplace. newlineThis study newlineassists us to find out how virtual team members can overcome Individual newlineand Organizational Challenges towards diversity and to find out the social newlinerelations among virtual team members, how much trust exists among them. -
A Study of fractal properties of turbulent functions
The studies have been mainly done in the discrete dynamical systems in topological spaces, we study the various types of relationships between chaotic functions and turbulent functions, a study of turbulent newlinefunctions in metric spaces, and the fractal nature of turbulent functions. In connection with this we study the relationship between turbulent function and chaos, also the relation between fractals and chaos. First newlinepart of the work gives a holistic outlook over the concepts like Turbulent Functions, Chaos and Fractals.Viewing the relevance of studying the irregular sets in the present and future scenario, we started our work newlinefocusing on the fractal nature of turbulent functions. The study incorporates the concepts like turbulent function, chaos and fractals along with rapid fluctuations. According to Robert Devaney, the three ingredients of chaos are sensitivity, density and transitivity. Rapid fluctuations newlineare very much connected with sensitive functions and turbulent functions. We could not and any implied relation between turbulence and sensitive functions. So we study the other two ingredients of chaos, newlinetransitivity and density. We answer a series of questions like whether the iterated function system can be chaotic. Will the contractions are Devaney chaotic. If we can find such a chaotic contraction, will it generate a self similar set? If there is such a self similar set, will it be a fractal? In order to answer the question, we have gone for generalization with continuous maps and homeomorphisms. Hence we study the fractal properties of turbulent function in a topological view point.The question that we have faced during the discussion and study of fractal properties of turbulent function is that whether a given turbulent function newlinef in a compact metric space provides an attractor and if there is an attractor, will it be a fractal. -
Factors Influencing Equity Investment Intention: A Behavioral Perspective
Many financial and psychological factors influence equity investment decisions. The present study examines the influence of Personality, risk attitude, and financial literacy on equity investment intention. Questionnaire responses were collected from Bengaluru investors. The present study uses the Big Five Personality Traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience) to categorise individual behaviour tendencies. Risk attitude is examined as a mediator variable, and financial literacy (Financial Knowledge, Financial Skill, and Financial Attitude) is examined as a moderator variable. The results show that extraversion, conscientiousness, and openness to experience positively affect equity investment intention, and Neuroticism negatively affects equity investment intention. Risk-taking propensity also moderates the personality-investment intention relationship and shows that individuals with high risk-taking propensity invest in equities. Financial literacy also moderates the relationship and implies that financial knowledge and ability are key determinants of investing. These results have policy and practice implications for investment educators, policymakers, and financial planners and indicate the value of investor-specified advice founded on psychological and financial literacy profiles. Financial literacy programs can assist investors in making effective investment decisions and managing risk. This research contributes to the behavioural finance literature by integrating personality psychology and financial literacy as investment decision-making frameworks. 2025, Iquz Galaxy Publisher. All rights reserved. -
Apparel shopping styles of young adult consumers in Bangalore
Apparels are one of the most frequently purchased product categories where young adults have the authority to make independent buying decisions, and they also become trendsetters and opinion leaders. Understanding this large segment appropriately is crucial for apparel manufacturers and marketers as they promise longevity of market and exert substantial influence on their parents, peers, as well as their own spending. The present study segmented young adult consumers based on their shopping styles towards purchase of apparels and explored the differences in the shopping styles across demographics such as gender, educational levels, and regional background. The respondents for the study were young adults who belonged to the age group of 18 - 25 years residing in Bangalore, India. The variables under study were eight shopping styles adapted from Sproles and Kendall Consumer Style Inventory- CSI (1986). The study revealed that all the eight shopping styles of the CSI were manifested among young adults in Bangalore; however, the predominant shopping style was the Perfectionist/ High Quality Conscious shopping style. Furthermore, significant differences in the shopping styles of young adults across gender, educational levels, and regional background were found. -
Automated segmentation and classification of nuclei in histopathological images
Various kinds of cancer are detected and diagnosed using histopathological analysis. Recent advances in whole slide scanner technology and the shift towards digitisation of whole slides have inspired the application of computational methods on histological data. Digital analysis of histopathological images has the potential to tackle issues accompanying conventional histological techniques, like the lack of objectivity and high variability. In this paper, we present a framework for the automated segmentation of nuclei from human histopathological whole slide images, and their classification using morphological and colour characteristics of the nuclei. The segmentation stage consists of two methods, thresholding and the watershed transform. The features of the segmented regions are recorded for the classification stage. Experimental results show that the knowledge from the selected features is capable of classifying a segmented object as a candidate nucleus and filtering out the incorrectly identified segments. Copyright 2022 Inderscience Enterprises Ltd.