Browse Items (9795 total)
Sort by:
-
An Examination of the Challenges Associated with Applying Artificial Intelligence Techniques to Specific Management Problems
Artificial intelligence (AI) holds immense promise in revolutionizing management practices across various sectors, offering solutions to complex problems and optimizing decision-making processes. However, the application of AI techniques to management problems is not without its challenges. This examination delves into the multifaceted hurdles encountered when integrating AI into management frameworks, highlighting key obstacles and potential avenues for overcoming them.AI algorithms heavily rely on large volumes of high-quality data for effective training and decision-making. Yet, many management domains grapple with disparate data sources, inconsistencies, and incomplete datasets, hindering the performance and reliability of AI systems. Furthermore, the dynamic nature of management problems poses a significant challenge to AI implementation. Management environments are characterized by evolving trends, uncertainties, and unforeseen disruptions, rendering static AI models inadequate in adapting to changing conditions. Hence, the development of agile AI systems capable of continuous learning and adaptation becomes essential for addressing the dynamic nature of management challenges. 2024, Collegium Basilea. All rights reserved. -
An Expected Model of Management Program in India
Pravara Management Review, Vol 15, Issue 2, pp. 17-23, ISSN No. 0975-7201 -
An Experimental Investigation on Flexural Strength of Ferrocement Slab Made of Slag Sand Partially Replaced with Iron Ore Tailings
Effective use of slag sand and Iron Ore Tailings and other waste obtained from the manufacturing industry and mining industry like waste foundry sand, will reduce the negative impact on the environment and also will provide opportunities for effective use of natural resources and contribute to sustainability. The aim of this research project is to study the flexural strength of ferrocement slab made of slag sand partially replaced with iron ore tailings with sustainability point of view. Investigation of 48 slab panels of 700mm 300 mm size with thickness 25 mm and 30 mm was conducted using 1 and 2 layers of weld mesh reinforcement casted with different percentage of iron ore tailings. Slabs were tested in Universal Testing Machine, which showed good results with 15% of iron ore tailings. Published under licence by IOP Publishing Ltd. -
An experimental investigation to study the performance and emission characteristics of n-butanol-gasoline blends in a twin spark ignition engine
The need of a substitute for the fossil fuels has gained maximum importance in the recent days with the depletion of fossil fuels, increasing vehicle population, enforcement of strict pollution norms to ensure a better environment for the present and future generations. Researchers around the world have investigated many fuels for IC engines and have found that alcohols exhibit properties that closely resemble the properties of gasoline. Alcohols form a stable mixture with gasoline in almost all proportions. This property of alcohol has increased its popularity as a fuel blend with gasoline. This paper aims at presenting the performance characteristics of a twin spark ignition engine fuelled with the blends of n-butanol-gasoline. In this investigation, pure gasoline (B00) and blends of gasoline with n-Butanol forms the fuel for twin spark ignition engine. The use of B35 blend, lower carbon monoxide emissions, lower unburnt hydrocarbon and lower nitrogen oxide emissions are observed as compared to pure gasoline. With these investigational results, one can arrive at the conclusions that with the use of higher blends of n-butanol-gasoline, the emission of the regulated emissions are reduced and are seen to be optimal with B35 in a twin spark ignition engine. TJPRC Pvt. Ltd. -
An experimental study on utilisation of red mud and iron ore tailings in production of stabilised blocks
Construction of bricks using waste materials is one among the many ways to address the problems encountered in infrastructure. In the present study, various industrial and mining wastes have been used to manufacture stable bricks. These wastes include red mud (RM) from Hindalco, and iron ore tailings (IOT) from BMM Ispat, Bellary. Both RM and IOT were combined in different proportions with ground-granulated blast furnace slag (GGBS) and waste lime. In first series, IOT was replaced in the range of 45% to 60% with increments of 5%, and RM was replaced in the range of 15% to 30% with increments of 5%. In the second series, RM was replaced in the range of 45% to 60% with increments of 5%, and IOT was replaced in the range of 15% to 30% with increments of 5%. Tests were performed as per the Indian and ASTM standards on both the raw material and the developed composites. These tests include liquid, plastic limit, particle size, XRF, XRD, and SEM on raw materials, while tests performed on composites were compressive strength, water absorption, efflorescence, porosity, apparent specific gravity, and bulk density. Results of the study indicate that addition of IOT up to 55% is acceptable as brick material. Springer Nature Singapore Pte Ltd 2020. -
An exploration of 'pull' and 'push' motivational factors among transgender entrepreneurs
To date, studies have focused on the men and women entrepreneurs and the gender difference in motivations among cisgender entrepreneurs. The study aims to determine whether a transgender individual entrepreneur is motivated through a push motivational factor or a pull motivational factor. This study employs a qualitative approach uses face-to-face interviews and a semi-structured interview with a sample size of 16 transgender entrepreneurs in India. It was found that the participants in this study were motivated by both push and pull factors. The motivational factors, which add to the knowledge of already existing push and pull factors, were to forego begging and commercial sex work, to break stereotypes, to create a business opportunity for other transgender individuals, to earn respect from society, to prove entrepreneurship is non-binary, to be a role model for other transgender individuals and to the society. In contrast, the push motivational factors were the limited opportunities, support received from society, the hijra guru, media, government support, family, friends, landlords, NGOs and another push motivational factor was the exhibitions conducted exclusively for the transgender individual entrepreneurs. 2025 Inderscience Enterprises Ltd. -
An exploration of attitudes toward dogs among college students in Bangalore, India
Conversations in the field of anthrozoology include treatment and distinction of food animals, animals as workers versus pests, and most recently, emerging pet trends including the practice of pet parenting. This paper explores attitudes toward pet dogs in the shared social space of urban India. The data include 375 pen-and-paper surveys from students at CHRIST (Deemed to be University) in Bangalore, India. Reflecting upon Serpells biaxial concept of dogs as a relationship of affect and utility, the paper considers the growing trend of pet dog keeping in urban spaces and the increased use of affiliative words to describe these relationships. The paper also explores potential sex differences in attitudes towards pet and stray dogs. Ultimately, these findings suggest that the presence of and affiliation with pet dogs, with reduced utility and increased affect, is symptomatic of cultural changes typical of societies encountering the second demographic transition. Despite this, sex differences as expected based upon evolutionary principles, remain present, with women more likely to emphasize health and welfare and men more likely to emphasize bravery and risk taking. 2019 by the authors. Licensee MDPI, Basel, Switzerland. -
An exploration of python libraries in machine learning models for data science
Python libraries are used in this chapter to create data science models. Data science is the construction of models that can predict and act on data, which is a subset of machine learning. Data science is an essential component of a number of fields because of the exponential growth of data. Python is a popular programming language for implementing machine learning models. The chapter discusses machine learning's role in data science, Python's role in this field, as well as how Python can be utilized. A breast cancer dataset is used as a data source for building machine learning models using Python libraries. Pandas, numpy, matplotlib, seaborn, scikitlearn, and tensorflow are some Python libraries discussed in this chapter, in addition to data preprocessing methods. A number of machine learning models for breast cancer treatment are discussed using this dataset and Python libraries. A discussion of machine learning's future in data science is provided at the conclusion of the chapter. Python libraries for machine learning are very useful for data scientists and researchers in general. 2023, IGI Global. All rights reserved. -
An exploration of the impact of Feature quality versus Feature quantity on the performance of a machine learning model
About 0.62 trillion bytes of data are generated every hour globally. These figures have been increasing as a result of digitalization and social networks. Some data ecosystems capture, store, and manage this big DATA. The basis is to be able to analyze their information and extract their value. This fact is a gold mine for companies researching and using this data. This leads us to follow how essential and valuable data is in this growing age. For any machine learning model, the selection of data is necessary. In this paper, several experiments have been performed to check the importance of data quality vs. data quantity on model performance. This clearly indicates comparing the data's richness regarding feature quality (e.g., features in images) and the amount of data for any machine learning model. Images are classified into two sets based on features, then removing redundant features from them, then training a machine learning model. Model getting trained with non-redundant data gives highest accuracy (>80%) in all cases versus the one with all features, proving the importance of feature variability and not just the feature count. 2023 IEEE. -
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 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.