Browse Items (14421 total)
Sort by:
-
Using Academic Performance Indicator to Evaluate the Cost to Company of Management Graduates
As the placement season hits CBS Business School, India, the pressure to get placed is at its peak. As the placement season draws to a close, the unplaced students storm the Directors office complaining about unfair treatment in the process. They lay blame on the random shortlisting followed by the Placement co-ordinator. Concerned with these allegations, the Director calls on faculty to investigate the situation. During the conversation one of the students, Rachit, expresses regret in not focusing solely on academics and instead on developing a more well-rounded profile. He feels that that is the reason for his failure to get placed. A fundamental question arises of how closely academic performance and Cost to Company (CTC) are related. Data is collected to examine the validity of the long-held belief that higher academic performance leads to higher paying job placement. 2022 NeilsonJournals Publishing. -
Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India
Coronavirus is airborne and can spread easily. Air pollution may have an impact on breathing and also keep the virus airborne. The levels of air pollution were impacted by the lockdown measures, restricting the vehicular and industrial pollutants. Therefore, there is a need to understand the relation between air pollution levels and the Coronavirus infection rate. The study aims to find the effect of various pollutants across major cities of India on the R-value. The pollution data was collected from the Governments official portal. The major pollutants on which the data was collected are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The data on air pollution levels were also collected for the selected cities from April 2020 to April 2021. The spread is measured as the reproduction number at time t (Rt), which is an estimate of infectious disease transmissibility throughout an outbreak, or it is the rating of Coronavirus or any diseases ability to spread. The data is analysed using MS Excel and R Programming. Descriptive statistics and regularisation are performed on the data. The study results reveal that some pollutants positively and negatively affect the infection rate. However, the effect is very low, and it concluded that the pollution might not directly affect infection rates. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Using Behavioural Economics to Analyse and Enhance Contraception Usage Decisions
Existent literature helped narrow down variables influencing modern contraceptive adoption (usage), a behaviour carrying enormous positive externality. Using finite population sample size formula and probability proportional to size method of sample selection, primary data was collected from participants using inclusion and exclusion criterions. Binary logistic regression model was used to predict probability of occurrence of dependent variable usage of modern method of contraception being treated at a dichotomous outcome level. Predictor variables after confirming association by cross tabulation were introduced stepwise to build model subject to elimination of those variables adding insignificantly to the overall predictability of the model. Variables such as gender, education level, spousal influence, extended family influence, financial well-being and contraceptive information were found to significantly predict the probability of occurrence of the dependent variable. Except for financial well-being with three sub-categories, other independent variables were treated at dichotomous level. Income level was found to be an important predictor although found statistically insignificant. Non-contributory factors such as age, occupation and years of marriage were dropped. Post-model construction, borrowing nudges from behavioural economics (BE) domain, strategies to nurture the significant context specific influencing variables, were articulated. BE was particularly preferred for its openness to the paradigm of non-rational behavioural choices. 2022 SAGE Publications. -
Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets
Suicidal detection and treatment from the clinical and public health perspective are reactive. For an action whose consequences are irreversible, a reactive approach to the problem cannot be the answer. A proactive approach is needed to solve and detect suicidal intent. Social media has become the television and diary of millennials and Gen z alike; hence, it is imperative to create techniques and approaches to study their actions in this particular space. This research involved creating document similarity algorithms from Corpora mined from the Twitter Developer API. Making the data unique to this platform, a methodology design involving validating data at various spectrum and selecting an appropriate threshold to classify the similarity levels were created as well as a lexicon unique to the Twitter Dataset. With an accuracy score of 84%, the Jaccard document similarity algorithm was able to spot suicidal intent from users tweets, and with an accuracy of 93%, it was also able to spot non-suicidal intent. The Jaccard model seemed to be the most durable and computationally efficient for the problem and was chosen as the algorithm for detecting suicidal tendencies in users tweets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Using Ensemble Model to Reduce Downtime in Manufacturing Industry: An Advanced Diagnostic Framework for Early Failure Detection
The fourth industrial resolution marks a significant shift that uses emerging technologies such as intelligent automation, extensive machine-to-machine communication, and the internet of things (IoT) to modernize conventional manufacturing and industrial methods. The examination of vast data gathered in modern industrial facilities has not only greatly leveraged artificial intelligence (AI) tools but has also driven the development of innovative technologies. In this context, a novel framework for predictive maintenance in the production sector is introduced in this research, which depends on an ensemble model. First, a set of input features are collected from sensors. Then, data normalization technique is applied to standardize and prepare data for further analysis. These normalized input features are then used to train an ensemble classifier. In the ensemble model, multilayer perceptron (MLP), K-nearest neighbors (KNN), and support vector machine (SVM) are serve as base classifiers. Efficacy of the designed framework is validated using predictive maintenance dataset. Results demonstrated that the proposed ensemble model exhibited improved accuracy compared to individual base classifiers. The results further demonstrated that the implemented model had superior efficiency compared to the other benchmark models. 2025 selection and editorial matter, Amit Kumar Tyagi, Shrikant Tiwari, and Gulshan Soni; individual chapters, the contributors. -
Using Fog Computing to Accelerate Metagenomic Data Analysis
This article discusses the challenges of processing and analyzing metagenomic data, the volume of which is continuously increasing due to the development of sequencing technologies. Traditional methods such as cloud computing and supercomputing face limitations such as high latency, network dependency, high costs and data security risks. Alternatively, fog computing and hybrid architectures are proposed to distribute the computational load between local devices and cloud systems. This reduces latency, optimizes costs and improves data security. The paper analyzes the advantages of fog computing in metagenomic data analysis, compares it with traditional methods and suggests ways to implement this technology in bioinformatics. The results show that fog computing systems and hybrid systems are promising solutions for applications requiring fast analysis and high data security, such as medical diagnostics and environmental monitoring. The complexity of integrating and managing distributed systems 2025 IEEE. -
Using Internal Resources for Transition From Temporary to Sustained Competitive Advantage
Since the conception of these terms, 'Competitive advantage' and 'sustainable competitive advantage' have been used interchangeably by many. The focus here is to answer the question, whether competitive advantage is always temporary, owing to the dynamic environment all around, or it can be sustained. In this chapter we also discuss if competitive advantage be measured without the time factor being a constrain. In this chapter, we discuss at length the meaning of Competitive advantage, the different researchers' viewpoints on Sustainability of competitive advantage, role of internal resources in transitioning from temporary to sustained Competitive advantage and how human resource plays an important role in encashing its intangible heterogeneity to sustain competitive advantage. 2025, IGI Global Scientific Publishing. All rights reserved. -
Using Machine Learning Algorithms to Personalize Customer Experience in Ghost Kitchens: Hyper-Personalized Marketing and Promotion
The emergence of ghost kitchens has revolutionized the food delivery industry by leveraging machine learning algorithms to enhance customer satisfaction and personalized experiences. This chapter, examines how predictive analytics identifies customer preferences, helping ghost kitchens create effective marketing strategies aligned with tastes and behaviours. It highlights real-time personalization, where offers are tailored based on past orders, location, and time, fostering relevance and loyalty. AI-driven customer segmentation is explored as a vital tool for precise targeting. At the same time, the chapter also addresses how AI assesses campaign performance to refine marketing tactics and adapt to changing demands. This research adds new fresh knowledge to the established body of knowledge in the context of restaurant food consumption behavior (Maziriri, E. T., Rukuni, T. F., & Chuchu, T. (2021)). This chapter is going to explore how AI advancements revolutionize resource utilization, evolving customer preferences, sustainable growth. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Using machine learning architecture to optimize and model the treatment process for saline water level analysis
Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. 2023, IWA Publishing. All rights reserved. -
Using Machine Learning Sentiment Analysis to Evaluate Students Learning Impact
For educational experiences and results to be improved, learning impact assessment is essential. Students' emotional reactions, which are crucial to their involvement and understanding, are frequently missed by traditional evaluation techniques. Through a review of student feedback, conversations, and course ratings, this study investigates the use of machine learning-based sentiment analysis to assess the impact of learning. Performance evaluations were conducted on a number of sentiment categorization models, including Nae Bayes, Support Vector Machines (SVM), Logistic Regression, Random Forest, Long Short-Term Memory (LSTM), and BERT. With an accuracy of 91.7%, the results show that BERT performs better than other models and offers more accurate sentiment classification. Accuracy and insights are further improved by combining textual, auditory, and visual signals in multi-modal sentiment analysis. The results show how sentiment analysis may be used to track feedback in real time facilitating adaptive learning techniques to raise student interest. Future studies should concentrate on expanding sentiment analysis applications to traditional and hybrid learning contexts, integrating multi-modal data, and ethical implications. 2025 IEEE. -
Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability
Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the energy. For weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions. 2025 IEEE. -
Using Sentiment Analysis to Identify Consumers Emotions in the Hotel Industry
This research attempted to present a more comprehensive overview of online user-generated data by extending far beyond quantitative analysis. We gathered a distinctive and substantial database of online user ratings for the hotel industry from numerous websites over a significant amount of time. To gauge the quality of hotel service, we divided customer reviews into two categories using the sentiment analysis technique. The impact of those factors in influencing users overall evaluation and content creation behavior is then investigated. The findings imply that different aspects of user evaluations have considerably diverse effects on how users evaluate products and what motivates them to create content. 2025 by Apple Academic Press, Inc. -
Using service learning to fuel multi-disciplinary research in Indian HEIs: A novel approach
The current work proposes a novel approach that can allow Indian HEIs to offer service-learning-based curricula while enhancing the institutional research output. The proposed model suggests a unique linkage between existing volunteer and academic departments at institutions of higher education such that data already being generated through existing outreach programs can be utilised for meaningful social science research. The proposed model thus utilizes existing resources already available with an institution to bolster research output, enhances the institutional capacity to include a pedagogical approach with proven benefits, and facilitates institutional compliance with regulatory directives mandating the inclusion of Service-learning-based courses at UG and PG levels. 2024, IGI Global. All rights reserved. -
Using Time series analysis, analyze the impact of the wholesale price index on the price escalation in the automotive industry
The automobile industry is a crucial sector of the economy, contributing significantly to employment and economic growth. One of the major challenges faced by this industry is the problem of price escalation, which can affect both consumers and manufacturers. In this project, we explore the impact of wholesale price index (WPI) on the price escalation of automobiles using time series analysis. We analyze the historical data of WPI and automobile prices in India from 2010 to 2022. We use statistical techniques like stationarity tests, autocorrelation analysis, and Granger causality tests to understand the relationship between WPI and automobile prices. Furthermore, we employ a SARIMA model in predicting WPI value and Vector Auto regression (VAR) model to analyze the dynamic interactions between WPI and CPI value. Our findings suggest that WPI has a significant impact on the price escalation of automobiles in India. The VAR model shows that there is a positive feedback loop between WPI, CPI and automobile prices, implying that an increase in WPI leads to a corresponding increase in automobile prices and vice versa. This feedback loop can create an inflationary spiral in the automobile industry, which can be detrimental to the economy. Our project highlights the importance of monitoring WPI and its impact on the automobile industry. Policymakers and industry experts can use our findings to develop effective strategies to manage price escalation in the automobile industry and mitigate its negative impact on the economy. 2023 ACM. -
Utilisation of Virtual Assistant and Its Impact on Retail Industry
Virtual assistant is nothing but an independent contractor, who offers administrative services to the clients of a particular organisation while operating outside of the office of the client. Generally, a virtual assistant operates from a home-based office. This virtual assistant application has the ability to access the required planning documents, such as shared calendars. The contemporary retail organisations like e-commerce companies in this competitive global business environment are using virtual assistant to enhance omnichannel experience, 24/7 customer service, order tracking, and product recommendations. Overall, virtual assistant helps the organisations in enhancing social media management activities. This concept of the use of virtual assistant has been significantly emerged after the increase in demands for e-commerce business activities in this decade. Research objectives related to the title of this research are developed and listed. Relevant theories on virtual assistant are applied in the literature review section of this study. The researcher has decided to adopt qualitative research methodology to achieve the objectives of the research. Moreover, the researcher has considered secondary data analysis approach to conduct this research. In terms of findings, it has been identified that virtual assistant has a positive impact on the business operation activities of retail organisations. Authentic secondary sources are considered to collect and analyse the data. Some challenges associated with the utilisation of virtual assistant also have been identified in the findings section. Some valuable recommendations are suggested for the future researchers to overcome those identified associated challenges. 2022 IEEE. -
Utilization of aluminum dross: Refractories from industrial waste
Aluminum oxide (Al2O3) and Magnesium-Aluminum oxides (MgAl2O4) are well known refractory materials used in engineering industries. They are built to withstand high temperatures and possess low thermal conductivities for greater energy efficiency. Dross, a product/byproduct of slag generated in aluminum metal production process is normally comprised of these two oxides in addition to aluminum nitride (AlN). Worldwide, thousands of tons of aluminum dross are generated as industrial wastes and are disposed of in landfills causing serious environmental hazard. This paper explores the potential to synergize the characteristics of the favourable contents of aluminum dross and its availability (in tons) via synthesis of refractories and thereby develop a value added product useful for the modern industries. In this work, Al-dross as-received from an aluminum industry which comprised of predominantly Al2O3, MgAl2O4 and AlN, was used to develop the refractories. AlN possesses high thermal conductivity values and therefore was leached out of the dross to protect the performance of the developed refractory. The washed dross was calcined at 700 and 1000C to facilitate gradual elimination of the undesired phases and finally sintered at 1500C. The dross refractory pellets were subjected to thermo-physical and structural properties analysis: XRD (structural phase), SEM (Microstructure), EDS (chemical constituents) and thermal shock cycling test by dipping in molten aluminum and exposing to ambient (laboratory). The findings include the favourable prospects of using aluminum dross as refractories in metal casting industries. Published under licence by IOP Publishing Ltd. -
Utilization of CO2 for Electrocarboxylation of Benzophenone Using MXene-Based Electrodes: A Sustainable Approach
The significant rise in atmospheric carbon dioxide (CO2) levels has prompted the need to develop efficient methods for CO2 conversion and fixation methods. Electrocarboxylation reaction is a highly efficient and sustainable method for activating and utilizing CO2, yielding essential carboxylic acids and their analogues, which are important intermediates in the pharmaceutical and fuel industries. This research demonstrates the efficiency of 2D Ti3C2Tx and Ta2CTx MXene-modified carbon fiber paper electrodes (Ti3C2Tx/CFP and Ta2CTx/CFP) for CO2 fixation with benzophenone in a tetrabutylammonium bromide/acetonitrile (TBABr/CH3CN) medium, yielding benzilic acid. Ti3C2Tx/CFP exhibited superior electrocatalytic activity with a lower reduction potential for benzophenone at ?1.0 V and achieved a 72% yield of benzilic acid at an optimum current density of 50 mA cm-2. In comparison, Ta2CTx/CFP exhibited a cathodic peak at ?1.08 V, producing a 66% yield at 70 mA cm-2. The electron paramagnetic resonance spectrum substantiates the generation of reactive radical intermediates during the reaction. Ti3C2Tx/CFP showed robust structural stability with ?88% Faradaic efficiency and a turnover frequency of 1.90444 10-5 s-1, indicating its potential for CO2 fixation. 2024 American Chemical Society. -
Utilization of industrial and agricultural waste materials for the development of geopolymer concrete- A review
Concrete is a highly consumed construction material. Cement is the first and foremost ingredient in the manufacture of concrete. Manufacturing of cement results in emission of an equal amount of carbon dioxide. These greenhouse gases cause global warming. The utilization of environment-friendly construction materials has been identified to be most essential to overcome environmental issues. An ecofriendly concrete such as geopolymer concrete founds to be an alternative for cement concrete. Geopolymer concrete (GPC) is a sustainable construction material as it can reduce carbon dioxide emission by utilizing industrial and agricultural waste by-products. Hence in this context, to reduce global warming, usage of cement can be minimized by replacing it with other materials such as Fly ash, Silica fume, Red mud, Ground granulated blast furnace slag, Metakaolin, Rice husk ash, Corncob ash, Sugarcane bagasse ash etc. These materials have been utilized to prepare geopolymer concrete with good mechanical strength, durability and thermal resistivity. A lot of research has gone into the development of sustainable geopolymer concrete utilizing various industrial and agricultural waste. This review paper is on the research on the utilization of industrial and agricultural waste materials to produce sustainable geopolymer concrete. 2022 -
Utilization of IoT-Based Healthcare System and Vital Data Monitoring of patients
The next generation of technology, known as the Internet of Things (IoT), will provide a comprehensive system that connects different domains, functions, and innovations. With the increasing demand for elderly care due to the growing ageing population, health monitoring systems have become increasingly important. Continuous monitoring is required in ICU to monitor the health conditions of patients. In cases where patients are released from the hospital, they are advised to rest and observed for a certain period, and the IoT system is very helpful in such cases. This article primarily discusses the implementation of a precise autonomous medical facility management system using IoT. In the past, only current data was displayed, and the patients history could not be accessed. In this study, we propose an IoT-based healthcare system for continuous monitoring of a patients health conditions. The healthcare system focuses on measuring and monitoring various biological parameters of the patients body, such as heart rate, blood oxygen saturation level, and temperature, using a web server and an Android application. Doctors can continuously monitor the patients condition on their smart phones using the Android application. Moreover, the patients history will be stored on the web server, and doctors can access the information from anywhere without being physically present. RJPT All right reserved. -
Utilization of Iron Ore Tailings for the Production of Fly Ash - GGBS-Based Geopolymer Bricks
In India, million tons of manufacturing ravages such as ground-granulated blast furnace slag (GGBS), fly ash and mine tailings, are endangering. These ravages turn out to be injurious as they are landfilled close to the production sites and somewhere else. Since these manufacturing ravages include silica, alumina, calcium, etc., it is probable to formulate these as unprocessed resources to produce building substance which diminishes the carbon trace. In this circumstance, this analysis observes on utilizing iron ore tailings and slag sand as a substitution for clay or natural sand for the construction of steady geopolymer obstruct. Furthermore, in this analysis, geopolymer is utilized as a binder rather than cement. Expansion of geopolymer binder-oriented bricks with fly ash and GGBS has been implemented in this study. The analysis consists of automatic possessions of the geopolymer bricks. Sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) resolution have been employed as alkaline activators. The proportion of alkaline liquid to aluminosilicate solid quotient and fraction of binder encompass foremost control on the force of brick. The bricks were casted and cured at ambient warmth. The compressive strength was tested at 7, 14 and 28 days. 2017 World Scientific Publishing Company.