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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 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 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 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 machine learning in an industrial control network to improve cybersecurity operations /
Patent Number: 202241052879, Applicant: Abhijit Das. A machine-learning service, which receives data related to a plurality of features related to internet traffic metrics, processes said data by performing operations selected from among an operation of ranking at least one feature, an operation of classifying at least one feature, an operation of predicting at least one feature, and an operation of clustering at least one feature, and as a result, the method monitors online security threats. -
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 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 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 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 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 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 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 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 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. -
Uses of Generative AI for SAP HANA Data Management
This chapter examines the transformative role of generative artificial intelligence (AI) in enterprise analytics, with emphasis on the Generative Pre-trained Transformer (GPT) family and related attention-based architectures. In contrast to conventional machine-learning pipelines whose performance is constrained by task-specific supervision and rigid feature engineering generative models exploit large-scale self-supervised pre-training, enabling emergent reasoning and effective transfer across heterogeneous downstream tasks. We demonstrate these advantages through a pragmatic integration of GPT-class large language models (LLMs) within an SAP HANA environment. By fine-tuning the LLM on domain-specific SQL corpora and curated schema metadata, the system learns to synthesise syntactically correct, execution-ready SQL statements that align with the underlying business logic. This design obviates costly data-centralisation efforts: users can pose natural-language questions and obtain HANA-compliant queries over distributed enterprise data without deep knowledge of relational algebra or SAP-specific functions. Moreover, explicit injection of domain ontologies during fine-tuning improves semantic grounding and materially increases query-generation accuracy. A sales-reporting case study substantiates these claims, showing that the approach streamlines complex analytic workflows, reduces time-to-insight, and enhances report reliability. Collectively, the findings position generative AI as a catalytic technology for modernising enterprise data management and accelerating data-driven innovation. 2026 Ram Kumar Chenthur Pandian, Shanmuga Raju Sekar, Subrata Chowdhury, Muhammad Rukunuddin Ghalib, and Kassian T.T. Amesho. -
Users Perception and Barriers to Using Self-Driven Rental Bikes
The research study has two objectives. The first objective of this paper was to find users' perception towards self-drive rental bikes. The second objective was to identify the factors that act as barriers to users using self-drive rental bikes. The research was a formal and structured conclusive research type and used quantitative data analysis techniques. The study had a representative sample of 350 respondents. The population selected for this study were people of various demographics in Bangalore. We used judgemental sampling to decide on the right sample. In achieving both objectives, factor analysis was used to arrive at a minimum number of factors or dimensions. The major perception factors are: Economical Choice, Environmental Consciousness, Alternative Source of Transport, Rationality, and Convenience. The major barriers to using self-drive rental bikes are Safety Issues, Conservative Nature of Users, the Expensive Nature of Service, and the Difficulty in Using Mobile Applications. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
User-engaged critical thinking abilities through 360-degree virtual reality documentaries
This chapter investigates how selected 360-degree VR documentaries foster critical thinking abilities among journalism students. A qualitative methodology was employed, and group discussions were conducted for data collection. The analysis was based on the Watson Glaser Critical Thinking Appraisal test, where four components are considered parameters for measuring students' critical thinking abilities. The study measures the analytical abilities, reasoning, interpretation, and logical conclusions that students can draw using the 360-degree VR documentaries. The results indicate a positive reception of virtual reality technology in enhancing critical thinking abilities using 360-degree virtual reality documentaries. VR documentaries engage the audience with their storytelling format, which accelerates their thought process and increases the immersive experience of the medium. The data also reflects how VR can be a powerful medium for addressing sensitive issues with an intensity that can pull authorities into implementing powerful policies. However, this chapter also highlights the constraints associated with the medium, which future researchers and content creators should consider to mitigate the challenges. Additionally, the limited content availability is considered a drawback of the study, which restricts the medium from reaching potential audiences. This chapter marks a pioneering attempt in India to understand how 360-degree VR documentaries enhance critical thinking abilities. This chapter's positive results offer an opportunity for educators to adopt the technology to create hybrid classrooms, thereby increasing students' engagement in the learning process. 2025 Twinkle Sara Joseph, Kannan Subramani and Biju Kunnumpurath. All rights reserved. -
User Sentiment Analysis of Blockchain-Enabled Peer-to-Peer Energy Trading
A new way for the general public to consume and trade green energy has emerged with the introduction of peer-to-peer (P2P) energy trading platforms. Thus, how the peer-to-peer energy trading platform is designed is crucial to facilitating the trading experience for users. The data mining method will be used in this study to assess the elements affecting the P2P energy trading experience. The Natural Language Processing (NLP) approach will also be used in this study to evaluate the variables that affect the P2P energy trading experience and look at the role of topic modeling in the topic extraction using LDA. The findings show that the general public was more interested in the new technology and how the energy coin payment system operated during the trade process. This explanation of energy as a CC is an outlier that fits well with the conventional literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
USER SATISFACTION OF CHRIST UNIVERSITY WEB PORTAL
Apart from the various factors contributing to the use and adoption of technologys newness, system usability has gained immense relevance. Here the usability is often identified as the satisfaction of the user from the particular system in use. The researcher intends to analyse and study the user-satisfaction of Christ University students, primarily, from the Christ University web portal. By drawing definitions from the International Standard Organisation, the meaning of user satisfaction is understood as the extent to which a specific user can use the product effectively to achieve specific goals. The researcher will search for answers among students of Christ University by confronting them with them questionnaires on its information quality, user-friendly nature and the availability of information and details. -
User request scheduling for multimedia resource using improved fuzzy logic with hybrid lyapunov based algorithm in hybrid cloud
The hybrid cloud provides vast opportunity to access the varied resources for effective provisioning of services to its users. The proposed scheduling algorithm uses the K-Nearest Neighbor(KNN) to locate the current location of the user and the nearest available computing resource. The Improved Fuzzy Logic (IFL) is applied for improving the resource balancing so that the resources are better utilized for the scheduling process. The wastage of resource usage and ideal resource are reduced considerably. The HLA scheduling is applied with the IFL, and based on the waiting of the jobs; the slots are allocated with jobs for execution. All the jobs are executed successfully with minimized execution time and makespan of the workflow application request. The performances of three algorithms are measured with parameters such as execution time, makespan time, in millisecond (ms). The execution speed is measured as throughput in MIPS (Millions of Instruction per Second). The resource utilization and usage of VMs are increased in the proposed scheduling algorithm resulting in a less number of ideal resources and reduced application cost. BEIESP.