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An improved AI-driven Data Analytics model for Modern Healthcare Environment
AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years. 2024 IEEE. -
An Improved AI-Based Low Latency Data Transmission in 5G Communication Systems
This paper devised an advanced artificial intelligence (AI) solution for ultra-low latency data transmission in 5G networks. With increasing data rates and lower latency required in 5G networks, efficient methods for transmitting the maximum amount of data are necessary. We have developed an approach that uses AI algorithms so that data transmission can be done more optimally and help reduce latency, providing better overall performance. Our approach consists of several steps, in which we predict the traffic patterns using machine learning techniques in step 1 and allocate network resources accordingly. That helps reduce network congestion and speeds up data transmission. We also introduce deep learning algorithms to adjust the transmission parameters according to network conditions, reducing latency. We simulate our algorithm in 5G network scenarios to assess its performance. The comparison of the results shows that a very low latency was achieved for this design over the earlier methods. Our developed AI-based improved solution provides a potential key to low latency data transmission in 5G communication systems. Integrating AI methods makes the system not only perform better but also be able to adapt more easily when network conditions change. The next steps are to explore the improvements of algorithms and implement them practically in 5G networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An Impact of technology based constructivist teaching on acdemic achivement of IX standrad students of Bengaluru city
Modern education emphasizes on learner centered and joyful learning which is the newlineneed of the hour as well initiated by educationists and education commission. They opine that, children need to keep active throughout the teaching and learning process and encourage self-learning and independent learning. One such emerging practice is constructivist teaching. It has changed the educational practice and converted Passive Learner Centered Environment into Active Learner Centered Environment.Constructivism newlinebelieves that learning is not encouraged in zero ground but on previous experience and newlineprior knowledge. It is the beginning for construction of new knowledge. In the context of Indian school education, it is rightly accepted as one of the pedagogical practice in National Curriculum Framework 2005 and National Curriculum Framework for Teacher newlineEducation 2009. It is also duly adopted in school education and teacher education newlineprogramme of Karnataka. Apart from constructivist approach, technology and technology integration highly influence on present education system. Technology not used only for drill,practice, tutorial etc. but also for construction of knowledge. newlineIn this context, there is a need for technology integration in constructivist practice and to give new framework for learning, teaching as well as for learner centered education. newlineHence, this research is conducted to study An Impact of Technology Based Constructivist Teaching on Academic Achievement of IX Standard Students of Bengaluru City .The main objective of the study is to compare the effectiveness of Constructivist Teaching and Technology Based Constructivist Teaching on academic achievement of IX standard students in Social Science subject. newlineThe present study is experimental in nature newlinewith two equivalent group design. In this study purposive sampling technique is used. The sample comprised of 156 students studying in IX standard of two schools (Government newlineand Private School) of Bengaluru city affiliated to state board. -
An impact of antibacterial efficacy of metal oxide nanoparticles: A promise for future
Since its advent, nanotechnology has seen applications in diverse fields including the biomedical domain. Many metal oxide nanoparticles (NP) have shown good antimicrobial properties. Their small size and ability to inhibit a broad spectrum of bacterial species have made them promising candidates in our search of antimicrobial agents. Since, they don't target a specific protein in a microbial species, the chances of the microbe gaining resistance is also less. This is indeed a great advantage over antibiotics, most of which target specific proteins of bacteria. Most of the pathogenic bacteria have gained resistance against commonly used antibiotics. In this context there is a dire need of antimicrobials with a broader spectrum of action. Metal oxide nanoparticles like: ZnO NPs and CuO NPs easily fit into this category. They can suppress microbial growth by reactive oxygen species production, thereby causing damage to biomolecules, cation release, interactions with membrane and ATP depletion. One of the challenges with metal oxide NP is their cytotoxicity. Scientists are in search of degradable and less toxic metal oxide NP. The current review focuses on the relative advantages and limitations of various metal oxides NPs in inhibiting microbial growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
An impact of AI and client acquisition strategies in real capital ventures
In the contemporary business environment, marked by rapid changes, client acquisition stands out as a pivotal factor for companies aiming at sustained growth, particularly in sectors such as finance and real estate. The ability to attract and retain clients is not only a measure of a company"s current success but also a fundamental driver for its future viability. This study focuses on Real Capital Ventures LLP, a company operating at the intersection of finance and real estate, aiming to unravel the intricacies of its client acquisition strategies. The overarching goal is to conduct an exhaustive examination of the current approaches employed by the firm and provide nuanced recommendations for refinement. By doing so, the study aspires to contribute to the enhancement of the effectiveness of Real Capital Ventures LLP"s client acquisition, ensuring its continued success in a fiercely competitive market. 2024 by IGI Global. All rights reserved. -
An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches
Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed schemes accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively. 2023 Baqiyatallah University of Medical Sciences. All rights reserved. -
An ideal MBA syllabus model -An Indian perspective /
Sumedha Journal of Management, Vol.8, Issue 1, pp.155-173, ISSN No: 2277-6753. -
An ICT-integrated Modular Training Program Enhancing the Digital Research Skills of Research Scholars
The teaching profession in higher education demands strong research skills, and with rapid technological advancements, university teaching professionals must familiarize themselves with digital research skills. Thus, university teachers and PhD research scholars across the globe are eager to develop their digital research skills to enhance their work efficiency. Acquiring digital research skills on the job or during the PhD program has proven to be challenging. These skills assist higher education professionals in various ways, such as supervising doctoral students, conducting research, working on research projects, and publishing research articles. Thus, the present study attempted to provide ICT-integrated modular training (MT) to facilitate the higher education teaching faculty and PhD scholars with digital research skills. The study employed a repeated cross-sectional research design and measured the effectiveness of the MT through a single group pre and post-test design. Researchers conducted three modular training sessions annually on digital research skills over five consecutive years. In total, 300 scholars attended the training and participated in the pre-test, post-test, and satisfaction survey. Findings from paired sample t-tests (t-value varied between 4.117 to 7.525, p < 0.05) revealed that modular training has been significantly effective with a large effect size (d > 0.8). Furthermore, the satisfaction survey revealed a high degree of satisfaction among participants. Future research may explore ways to strengthen the technological and pedagogical content knowledge of modular training programs in developing digital research skills. Italian e-Learning Association. -
An iconic turn in philosophy
[No abstract available] -
An hybrid technique for optimized clustering of EHR using binary particle swarm and constrained optimization for better performance in prediction of cardiovascular diseases
The significant adoption of Electronic Health Records (EHR) in healthcare has furnished large new quantities of information for statistical machine gaining knowledge of researchers in their efforts to version and expects affected person health popularity, doubtlessly permitting novel advances in treatment. Unsupervised system learning is the project of studying styles in facts where no labels are present. In comparison to loads of optimization problems, an most beneficial clustering end result does not exist. One-of-a-kind algorithms with special parameters produce special clusters, and none can be proved to be the quality answer given that numerous good walls of the records might be found. In the previous work, a novel Two-fold clustering technique which uses the Long Short Term Memory (LSTM) technique (TFC: LSTM) for the prediction of Cardiovascular Disease (CVD) was proposed. The proposed model was fond to be experimentally efficient; however when applied to large EHR data, the model suffered from optimization issues on the number of clusters formed and time complexity. In order to overcome the drawbacks, this paper proposes a hybrid method of optimization using the Binary Particle Swarm (BPS) and Constrained Optimization (CO) for optimizing the number of clusters produced and to increase the efficiency in terms of decreasing the time complexity. 2022 The Authors -
An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
Evaluating the tissues responsible for complicated human illnesses is important to rank significance of genetic revision connected to features. In order to make predictions about the regulatory functions of geneticsvariations athwart wide range of epigenetic changes, this article introduces a Convolutional neural network (CNN) model upgraded filters and Deepsec framework incorporated with comprehensive ENCODE and Roadmap consortia have compiled a human epigenetic map that indicates specificity to certain tissues or cell types. Deepsec framework integrates transcription factors, histone modification markers, and RNA accessibility maps to comprehensively evaluate the consequences of non-coding alterations on the most important components, even for uncommon variations or novel mutations. By using trait-associated loci and more than 30 different human pancreatic islets and their subsets of cells sorted using fluorescence-activated cell sorting, annotations of epigenetic profiling were obtained (FACS) on a genome-wide scale. The proposed model, used '1492' publicly available GWAS datasets. My team presented that deepsec framework does epigenetic annotations found important GWAS associations and uncover regulatory loci from background signals when exposed to CNN-based analysis, offering fresh intuition underlying nadir causes of type 2diabetes. The suggested approaches are anticipated to be extensively used in downstream GWAS analysis, making it possible to assess non-coding variations and conduct downstream GWAS analysis 2023 IEEE. -
An Extensive Time Series Analysis of Covid-19 Data Sets on the Indian States
Pandemic influenza coronavirus is causing a great loss to mankind. It is creating a chaos on the global economy. Fight against this unseen enemy is affecting all the sectors of the global economy. Mankind is quivering with fear and scared to do something. This study gives a detailed presentation of the current position of virus escalation in India. Sentiment analytics from Twitter data is evaluated on sentiment, emotions and fear opinions are analyzed in the study. The analysis is on red, orange and green zones in several states of India and also gave a comprehensive interpretation on various phases of lockdown. Confirmed, active, recovered and deceased cases in all states are modeled to predict the increase of number of cases. Textual, geographical and graphical analytics are extensively described in the research study. Time series analysis is broadly elaborated as a case study till July 22, 2020, forecasting the impact of virus on Maharashtra, Kerala, Gujarat, Delhi and Tamil Nadu. This study will favor the administrative system to control the disease spread across the nation. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An extensive review on transition metal catalyzed indole C[sbnd]H activation: Catalyst selection and mechanistic insights
The present review article explores the expansive synthetic methodologies facilitated by C[sbnd]H activation of indoles using transition metal catalysts. The strategic utilization of catalysts such as palladium, rhodium, iridium, ruthenium, and manganese has revolutionized organic synthesis by enabling selective alkynylation, acylation, and annulation reactions. These transformations are pivotal in pharmaceuticals, particularly in the synthesis of antihistamines and potential antiviral drugs against SARS-CoV-2. Additionally, these catalysts play a crucial role in perfumery and other chemical industries, enhancing the efficiency and precision of compound synthesis. The choice of transition metal catalysts is informed by their affordability and compatibility with both traditional analytical methods and innovative techniques like microwave synthesis and LED irradiation. Furthermore, this review underscores the interdisciplinary impact of transition metal-catalyzed C[sbnd]H activation on indoles, highlighting its significance in advancing both fundamental organic chemistry and applied sciences essential for modern technological advancements and drug discovery efforts. 2024 The Author(s) -
An extensive critique on expert system control in solar photovoltaic dominated microgrids
Solar and wind power have recently become a potential option in power systems and act significantly to meet load penetration demands. The present growth of such renewable energy sources has shown an exponential increase. The high penetration of such system helps a grid effectively meet its load in an irregular demand but also creates some disturbances in the grid due to frequent additions and detachments of load or source. The way by which the renewable energy sources usually work in the on-grid mode is to be attached to and cut down from the grids without creating disturbances in a stable grid. Another important requirement is effective load management with fewer transmission losses. This article presents a detailed review of a microgrid and enumerates the possible methods for the analysis of the system, feature extraction, control methods, and options for machine learning. This paper examines the factors affecting the operations in a power system, their nature, interdependability, and controllability. It also inspects the various machine learning algorithms, their feasibility, and possible applications in power systems. The major contribution of the paper is the elucidation of expert system control methods for the performance improvement of solar PV assisted DC microgrids. The major objective of the paper is to provide an overview on various algorithms intended for the microgrid systems pertaining to its accuracy, precision, classification, prediction and forecasting. 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. -
An Extensive Analysis of Artificial Intelligence Integration in Management Approaches
Artificial Intelligence (AI) is now a strategic enabler across a large number of management domains in the digital transformation era. We conducted this review which analyzes AI and its integration into management by 990 peer reviewed publications from 2015 to 2025 from the Scopus database. It filtered studies on AI's role in strategy, HR, finance, operations and decision-making in a systematic manner. Latent Dirichlet Allocation (LDA) formed five key themes of predictive analytics, AI in HR, financial planning, intelligent decision systems, and explainable AI. The findings suggest that digital resilience needs drive the surge of AI related management research after 2019. This review points out emerging trends, difficulties in integrations, as well as critical insights which can orient the future research over such specific studies. 2025 IEEE. -
An exposition on complete androgen insensitivity syndrome and a case report; [??????? ?????? ?????????????????? ? ??????????: ??????????? ??????]
Complete androgen insensitivity syndrome (CAIS) is a rare X-linked sexual development condition typified by 46,XY karyotype, presence of external female genitalia along with intra-abdominal testes in labia majora or inguinal ring region. This syndrome results from alterations in the androgen receptor (AR) gene leading to primary amenorrhea and uterine agenesis (Mlerian agenesis) in adolescent teens or two-sided labial/inguinal hernia with testes in children around prepubertal age. Our paper reports a case of CAIS in a 16-year-old woman with no menarche and 46,XY karyotyping. Gonadectomy results showed hyperplasia of Leydig cells. The current research encompasses the case report and the available knowledge to date on the understanding, diagnosis, treatment, and management of CAIS. 2025 IRBIS LLC. All rights reserved. -
An exploratory study on video marketing as a new tool of advertising in digital marketing /
The rate at which New Media technologies are on the rise, directs us to the question how they affect the media business. In areas such as advertising, innovation come and go, resulting in marketing business development, at a faster pace. Today, the cutting edge technology of Video marketing had entered the advertising industry. Consumers are more mobile than ever before and advertisers want to get in touch with them while on the go. Amid all the new developments, video marketing is more and more essential for commodities and services. -
An Exploratory Study on Vancharya as a Therapeutic Approach to the Bio-field of Young Adults Using Electronic Photographic Imaging
Background: Amid the fast-paced world, nature has a therapeutic modality for healing individuals both physiologically and psychologically. One such practice mentioned in an ancient Indian text is Vanacharya, which provides a deep connection with nature and a means of achieving overall well-being. Vancharya is a practice with profound roots in Indian spiritual and philosophical traditions that view the environment as a sacred and valuable source of knowledge and healing. Purpose: This purpose of this experimental research is to explore the therapeutic benefits of vancharya, in healing subtle systems of energy or the biofield present within the body like Aura Field (AF), Overall Alignment of Chakra (OAC), Overall, Chakra Energy (OCE), Stress Level (SL), Overall Energy Level (OEL). Methods: This research aims to evaluate the effect of vancharya, by examining 50 young adult participants over a one-week period (7 days). The study utilised a non-experimental single-group pre- and post-research design. The data collection was done using an advanced Biowell machine. The obtained data were analysed through a Paired Sample t-test by using SPSS software. Results: The obtained results indicated significant changes in the AF, OAC, OCE, while showing no significant impact on participants SL and OEL. Subjects also reported improved sleep patterns, less impulsivity, reduced aggression and fewer fluctuations of mood during their sessions in day-to-day activities. Conclusion: Therefore, the research indicates that Vancharya as a therapeutic modality had a significant impact on the subtle systems of energy among young adults. The obtained result from this intervention programme clearly indicates that subtle systems present in the body can have an impact as early as within 7 days itself, whereas, for visible impact within the individual (for instance stress level or overall energy level of the body), the duration of the intervention can be increased. The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
An exploratory study of Python's role in the advancement of cryptocurrency and blockchain ecosystems
Blockchain is the foundation of cryptocurrency and enables decentralized transactions through its immutable ledger. The technology uses hashing to ensure secure transactions and is becoming increasingly popular due to its wide range of applications. Python is a performant, secure, scalable language well-suited for blockchain applications. It provides developers free tools for faster code writing and simplifies crypto analysis. Python allows developers to code blockchains quickly and efficiently as it is a completely scripted language that does not require compilation. Different models such as SVR, ARIMA, and LSTM can be used to predict cryptocurrency prices, and many Python packages are available for seamlessly pulling cryptocurrency data. Python can also create one's cryptocurrency version, as seen with Facebook's proposed cryptocurrency, Libra. Finally, a versatile and speedy language is needed for blockchain applications that enable chain addition without parallel processing, so Python is a suitable choice. 2023, IGI Global. All rights reserved. -
An Exploratory Study of Emotional Labour Among Therapists and Counsellors in India
Background: Emotional labour has been extensively investigated in the service sector, where employees manage their emotions to ensure a positive customer experience. However, there is a dearth of research into how therapists perform emotional labour during therapy sessions. Thus, the aim of this study was to explore psychotherapists' and counsellors' experiences of performing emotional labour in therapeutic settings. Method: The study used a qualitative research design with an exploratory approach. Semi-structured interviews were conducted with four clinical psychologists and four counsellors. The interviews were conducted via video call and lasted about 4560 min. Thematic analysis was used to identify emerging themes. Results: The analysis revealed that therapists experience an array of emotions during sessions. However, the expression of these emotions is guided by professional norms and emotional display rules. Participants disclosed that they use several techniques to manage their emotions both during and after sessions and that participating in emotional labour yielded both favourable and unfavourable outcomes for the therapists. Conclusion: The findings presented in this study provide insight into emotional labour and inform professionals on how this can negatively impact them if not sufficiently addressed. The study highlights the need for further investigation. In the meantime, therapists and counsellors would benefit from integrating the study's findings into their respective practices. 2025 British Association for Counselling and Psychotherapy.