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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 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 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 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 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 iconic turn in philosophy
[No abstract available] -
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 ideal MBA syllabus model -An Indian perspective /
Sumedha Journal of Management, Vol.8, Issue 1, pp.155-173, ISSN No: 2277-6753. -
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 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 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 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 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 Alternative Method of Imputation for Missing Data in Survey Sampling
In the present paper, a new and improved method of ratio type imputation and corresponding point estimator to estimate the finite population mean is proposed in case of missing data problem. It has been shown that this estimator utilizes the readily available auxiliary information efficiently and gives better results than the ratio and mean methods of imputation; furthermore, its efficiency is also compared with the regression method of imputation and some other imputation methods, discussed in this article, using four real data sets. A simulation study is carried out to verify theoretical outcomes, and suitable recommendations are made. 2022 NSP Natural Sciences Publishing Cor. -
An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
As object detection has gained popularity in recent years, there are many object detection algorithms available in today's world. Yet the algorithm with better accuracy and better speed is considered vital for critical applications. Therefore, in this article, the use of the YOLOV4 object detection algorithm is combined with improved and efficient inference methods. The YOLOV4 state-of-the-art algorithm is 12% faster compared to its previous version, YOLOV3, and twice as faster compared to the EfficientDet algorithm in the Tesla V100 GPU. However, the algorithm has lacked performance on an average machine and on single-board machines like Jetson Nano and Jetson TX2. In this research, we examine the performance of inferencing in several frameworks and propose a framework that effectively uses hardware to optimize the network while consuming less than 30% of the hardware of other frameworks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Improved Artificial Intelligence based Service Quality to Increase Customer Satisfaction and Customer Loyalty in Banking Sector
This study clarifies and determines how service quality affects customer loyalty and reliability. The support of quality in the open and private financial sphere and understanding of its connection to customer loyalty and conduct goal Utilizing an upgraded SERVQUAL (BANQUAL) tool with 26 items, the review was conducted among 802 bank customers. The social goal battery was used to estimate the clients' expected conduct. The expert used a seven-point Likert scale to assess the standard and saw service quality (implementation), as well as the social expectations of the clients. The most reliable tool to quantify the conceptualization of the differentiation score is the BANQUAL instrument. It is used to evaluate gaps in service between assumptions and perceptions of service quality. The SERVQUAL instrument is modified to make it suitable in the banking industry. Questions on parking at the bank, the variety of things and programmes available, and the banks' genuine efforts to address customer grievances are added to the instrument (Responsiveness). The writing audit was sufficiently compiled from many sources, reflecting both an Indian and foreign environment. The postulation included several hypotheses then examined using structural equation modelling. To meet the exploration goals, the views were tested using the products AMOS and SISS. The data were analysed using corroborative and explorative element research to confirm the BANQUAL instrument's dependability and legitimacy of the financial business execution and service quality aspects. The resulting CFA model value exhibits excellent psychometric qualities. Professional businesses and clients increasingly use artificial intelligence support specialists (AISA) for management. However, no measure measuring the support quality can fully capture the essential factors affecting AISA service quality. By developing a scale for evaluating the quality of AISA service, this study seeks to solve this deficiency(AISAQUAL). 2023 IEEE. -
An Improved Combined Adaptive Outline for Contrast Enhancement of Blood Vessels
Appropriate vascular segmentation is dependent on effective picture pre-processing techniques that improve the contrast of the blood vessels, reduce noise, eliminate non-uniform illumination, highlight thin vessels, and retain background texture. These techniques are necessary for accurate vessel segmentation. Here, both the edge- and texture-smoothed data from the vessel probability map are used in the derivation of the adaptive optimal q-order in the G-L mask. The smooth information is not affected, the textures are maintained, and the contrast of the blood vessels is enhanced, thanks to the proposed filter. In addition to sharpening the focus on the vessels themselves, a Gaussian curve fitting is used to contrast stretch the entire image. Retinal fundus images processed with cerebral DSA are subjected to both qualitative and quantitative assessments of contrast enhancement. Quantitative performance indicators are tabulated and compared to other approaches to show how well this technique works for improving medical images everywhere. The suggested filter is easy to implement, flexible enough to adapt to different images, and effective at increasing both vessel contrast and overall image contrast. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An improved compocasting technique for uniformly dispersed multi-walled carbon nanotube in AA2219 Alloy Melt
Technology transfer for economic bulk production is the greatest challenge of the era. Production of high strength lightweight materials with nanocarbon reinforcement has attained its importance among the researchers. Property enhancement with multi-walled carbon nanotube (MWCNT) reinforcement is reported by all researchers. But effective utilization of its property remains a challenge even though it is the strongest material in the world. Achieving homogeneous dispersion especially in molten metal is a complex task. To address the same, a new approach was tried which could trigger de-bundling and make a uniform dispersion. Various metallurgical and mechanical characterizations were done. Grain refinement and the structure were studied with an optical microscope, MWCNT dispersion and structural damage was studied using field emission scanning microscope, Phase change and reactions during casting was done with XRD scan. The method remarkably facilitated 23.7% and 69.75% improvement in hardness and ultimate compressive strength respectively with the addition of MWCNT. Faculty of Mechanical Engineering, Belgrade. -
An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree
Frequent itemsets are itemsets that occur frequently in a dataset. Frequent itemset mining extracts specific itemsets with supports higher than or equal to a minimum support threshold. Many mining methods have been proposed but Apriori and FP-growth are still regarded as two prominent algorithms. The performance of the frequent itemset mining depends on many factors; one of them is searching the nodes while constructing the tree. This paper introduces a new prefix-tree structure called child structured frequent pattern tree (CSFP-tree), an FP-tree attached with a child search subtree to each node. The experimental results reveal that the CSFP-tree is superior to the FP-tree and its new variations for any kind of datasets. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
An Improved Image Up-Scaling Technique using Optimize Filter and Iterative Gradient Method
In numerous realtime applications, image upscaling often relies on several polynomial techniques to reduce computational complexity. However, in high-resolution (HR) images, such polynomial interpolation can lead to blurring artifacts due to edge degradation. Similarly, various edge-directed and learning-based systems can cause similar blurring effects in high-frequency images. To mitigate these issues, directional filtering is employed post corner averaging interpolation, involving two passes to complete the corner average process. The initial step in low-resolution (LR) picture interpolation involves corner pixel refinement after averaging interpolation. A directional filter is then applied to preserve the edges of the interpolated image. This process yields two distinct outputs: the base image and the detail image. Furthermore, an additional cuckoo-optimized filter is implemented on the base image, focusing on texture features and boundary edges to recover neighboring boundary edges. Additionally, a Laplacian filter is utilized to enhance intra-region information within the detailed image. To minimize reconstruction errors, an iterative gradient approach combines the optimally filtered image with the sharpened detail image, generating an enhanced HR image. Empirical data supports the effectiveness of the proposed algorithm, indicating superior performance compared to state-of-the-art methods in terms of both visual appeal and measured parameters. The proposed method's superiority is demonstrated experimentally across multiple image datasets, with higher PSNR, SSIM, and FSIM values indicating better image degradation reduction, improved edge preservation, and superior restoration capabilities, particularly when upscaling High-Frequency regions of images. 2023 IEEE.