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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 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 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. -
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 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. -
User profiling based on keyword clusters for improved recommendations
Recommender Systems (RS) have risen in popularity over the years, and their ability to ease decision-making for the user in various domains has made them ubiquitous. However, the sparsity of data continues to be one of the biggest shortcomings of the suggestions offered. Recommendation algorithms typically model user preferences in the form of a profile, which is then used to match user preferences to items of their interest. Consequently, the quality of recommendations is directly related to the level of detail contained in these profiles. Several attempts at enriching the user profiles leveraging both user preference data and item content details have been explored in the past. We propose a method of constructing a user profile, specifically for the movie domain, based on user preference for keyword clusters, which indirectly captures preferences for various narrative styles. These profiles are then utilized to perform both content-based (CB) filtering as well as collaborative filtering (CF). The proposed approach scores over the direct keyword-matching, genre-based user profiling and the traditional CF methods under sparse data scenarios as established by various experiments. It has the advantage of a compact user model representation, while at the same time capturing the essence of the styles or genres preferred by the user. The identification of implicit genres is captured effectively through clustering without requiring labeled data for training. 2014 Springer International Publishing Switzerland. -
User Perception of Mobile Banking: Application of Sentiment Analysis and Topic Modelling Approaches to Online Reviews
The digital revolution has led to significant changes in the global as well as Indian banking sector. The introduction of mobile banking apps has provided increased convenience to customers, who can now avail various banking services remotely. Thus, it is imperative to study the customers' sentiments regarding these applications and find scope for improvement, so that customers can seamlessly operate their bank accounts without having to visit bank branches. Thus, the primary purpose of this research is to study the perceptions of customers towards mobile applications of six major banks in India. A sample of 3000 reviews left by users of these apps was scraped from Google Play Store and sentiment analysis was conducted using RoBERTa-base model from the Transformers library. This was followed by topic modeling using Latent Dirichlet Allocation to find the aspects that are most important to the users. Results revealed that user experience is majorly driven by customer support service, features and functionality of apps, and app performance. Our findings shall help banks identify key areas of improvement so that they can work on enhancing overall customer experience. Despite the growing popularity of mobile banking, this study is the first of its kind in Indian context. 2024 IEEE. -
USER EXPERIENCES OF CHATGPT AMONG ENGINEERING STUDENTS, TEACHERS, AND WORKING PROFESSIONALS IN INDIA
The introduction of Chat Generative Pre-Trained Transformer (ChatGPT) in November 2022 brought about rapid changes in the workplace and academia. Its usage ranged from student assignments to workplace targets in the engineering field. Although it has brought novel ideas to its application in various fields and task efficiency in the workplace, its perceived application varies among students, teachers, and professionals. This study employed the snowball sampling technique and interviews with eight students, eight faculty members, and eight working professionals from computer science engineering who used ChatGPT regularly. The study adopted a qualitative research design and employed the narrative data analysis technique. Researchers conducted in-depth, semi-structured interviews to elicit user experiences from the recruited samples. The findings brought out six main and twelve subordinate themes regarding ChatGPT user experiences: adapt, adopt, embrace, ease, speed, engage, and automate. The inclusion criteria involved ChatGPT users from the computer science engineering domain only. Future research may focus on developing ChatGPT user policies for various fields of their applications. 2024, Grand Canyon University. All rights reserved. -
User Authentication with Graphical Passwords using Hybrid Images and Hash Function
As per human psychology, people remember visual objects more than texts. Although many user authentication mechanisms are based on text passwords, biometric characteristics, tokens, etc., image passwords have proven to be a substitute due to its ease of use and reliability. The technological advancements and evolutions in authentication mechanisms brought greater convenience but increased the probability of exposing passwords through various attacks like shoulder-surfing, dictionary, key-logger, and social engineering attacks. The proposed methodology addresses these vulnerabilities and ensures to keep up the usability of graphical passwords. The system displays hybrid images that users need to recognize and type the randomly generated alphanumeric or special character values associated with each of them. A mechanism to generate One Time Password (OTP) is included for additional security. As a result, it is difficult for an attacker to capture and misuse the password. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Usefulness of Augmented Reality on Product Selection: An Experimental Study
Augmented Reality (AR) has brought a revolution in the business world. Most literature in augmented reality is concentrated on the acceptance, responses, and user-friendliness of AR applications. However, it fails to evaluate the ability of AR applications to aid the customer in product selection. Therefore, the primary aim of this study was to fill this gap in the literature by conducting an experimental study to evaluate the furniture selection enabled by AR application. The respondents for the study were grouped into two (experimental and control groups) and were asked to design a room. The respondents in the experimental group were asked to design a room by providing an AR application, and the control group was asked to design a room without an AR application. These designs were evaluated by 15 professionals on five parameters- harmony, volume, design, colour scheme and positioning. The ratings given by these professionals were analysed using a t-test. From the analysis, it was concluded that according to the interior designers' opinion, the AR application proves to be helpful to the customers in creating better room designs. These findings indicate that AR application increases customer ability to select appropriate furniture for designing their homes. Based on these findings, it can be suggested that the AR applications can be used in the furniture selection process for a better choice of furniture. 2022 SCMS Group of Educational Institutions. All rights reserved. -
Use of zeolite and industrial waste materials in high strength concrete - A review
Concrete is widely used in construction material by the construction industry. It is considered as a vital material because of its properties. Different grades of concrete (M10, M20, M30, M40, M50, M60and M70) are used in construction and are chosen based on the requirements. Higher grade concrete requires cement of different properties. The manufacturing process of cement, releases a huge amount of Carbon footprints. To reduce the emission of CO2, usage of virgin cement can be minimized by partially replacing with pozzolanic materials or industrial wastes like zeolite, metakaolin, silica fume and fly ash. These materials improve the durability and strength of concrete by filling the pores and reduce the porosity and permeability of the concrete without compromising on the desired properties. For sustainable development and protecting the environment, enormous research has been done on concrete by using various industrial waste materials. This article is a state-of-the-art review of research on the use of industrial waste materials to produce High Strength Concrete (HSC). Different materials were studied to prepare HSC by using distinct methods. Different experimental tests were conducted on concrete when cement is partially replaced with industrial waste materials and are compared with conventional concrete. It is observed that the partial replacement of cement with zeolite, metakaolin, fly ash, and silica fume, the properties of concrete increases up to certain age and mixing proportions when compared to conventional concrete. It is observed that there is limited research was done on zeolite with the combination of industrial waste materials for health analysis of the structures at different w/c ratios for large production. So, further investigation is needed on the technical, environmental, economic aspects and educating the public through the use of industrial waste materials as a sustainable approach. 2021 Elsevier Ltd. All rights reserved. -
Use of waste foundry sand in precast concrete paver blocksa study with belgaum foundry industry
The current study was undertaken at CHRIST (Deemed to be University) in Bangalore to investigate the potential of using waste sand from Belgaum foundries as fine aggregate in the production of precast concrete paver blocks. Concrete paver blocks were manufactured as per the recommendations of IS 15658:2006. M-35 grade of concrete with block thickness of 60mm was considered as the design parameter. Waste Foundry Sand (WFS) and ground-Granulated Blast Furnace Slag (GGBS) were replaced for manufactured sand and cement, respectively. WFS replacement rates were 15, 30, and 45% by weight of the manufactured sand, and that of GGBS was 30% constant by weight of cement. Obligatory performance tests were conducted as per Indian standards, which included compressive strength, water absorption, and abrasion resistance. Accordingly, paver blocks with 45% WFS showed satisfactory results and can be considered into non-traffic to light-traffic category, which finds application in places like building and monument premises, paths and patios, landscapes, public gardens, and parks. Cost comparison of conventional paver blocks with WFS paver blocks showed approximately 4.8% reduction in the cost of paver blocks containing 45% WFS. Springer Nature Singapore Pte Ltd 2020. -
Use of the internet of things in connected passenger car /
Patent Number: 202121053264, Applicant: Shilpa Meenor Lambor.
The aim of this work is to acquaint readers with the use of the Internet of Things in on-board systems of interconnected passenger cars. The partial goal is to present specific selected on-board systems using the Internet of Things and their mutual comparison. The theoretical part first defines the term internet of things together with the term connected passenger car and the very principle of communication of a connected passenger car. The theoretical part also includes the historical development of interconnected passenger cars and the current state of these cars. -
Use of social media applications in Indian governing policies /
New media is prevalent tool and a mass medium since it became associated with social networking. Apart from public relation companies and executives, Government agencies are also using new media like YouTube, Facebook and Twitter extensively. The study aims to depict of how social media and mobile applications are being used by governments to inform, engage and serve people. -
USE OF SELF ESTEEM APPEAL IN ADVERTISEMENTS THAT CATER TO WOMEN
The concept of Self-esteem appeals are considerably new in advertising, they are used in the advertisement to increase the buying and selling for products that are meant for women. The advertisers increase the demand of their products, with a particular target audience they pioneer ideas that will psychologically stimulate a strong feeling which will lead to a strong emotion that make them purchase the product or least take a good notice of the product addressed. The study aims to analyse the effectiveness of the advertisements that are made for products that are specially meant for the women, to find how these products affect the attitude towards looking at the product and themselves. And also to look into how the consumers recall the brand that uses a lot of self esteem appeal in them. -
Use of popular paintings in advertising /
Western paintings are constantly been in multiple mediums used because of its high recall value; these paintings have had a huge impact on global advertisement by multiple companies to promote their products and services. These popular western paintings have been the source of inspiration for various reason, the age old classic paintings have been deconstructed analysed and meanings have derived.