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Analysis of food consumption expenditure patterns for developing sustainable business practices
Food consumption patterns are said to be near ideal variable since it is one of the most important commodities that ensure human survival. The patterns within the consumption as well as between different types of food items to different levels of consumption varies with respect to the macroeconomic conditions. The consumption expenditure for cereals, pulses and pulse products, edible oil, milk and milk products, meat, fish and egg, vegetables and fruits, shows an increasing trend in India and in Karnataka. The income elasticity for all food categories are lesser in urban sectors of Karnataka than the rural sector which indicates that the income of the rural households is lesser and the increase in income is also lesser compared to that of the Urban-Karnataka. The business enterprises can aim at sustainable consumption practices through the pricing strategies, product design and distribution channels. 2021 Ecological Society of India. All rights reserved. -
Analysis of Fraud Prediction and Detection Through Machine Learning
In today's world the rate of fraudulent activities has significantly elevated, because of which a need for a competent system is required. Among all the fraudulent activities insurance fraud has the most dominating rate of growth. Fraud studies have suggested, that upon identifying the similar characteristics of a fraudulent claim with the claimants, a system of forensic and data-mining technologies for fraud detection can be set up. In this, seek to define fraud and fraudster, and look at the types of fraud and followed by the consequences of fraud to financial systems. As fraud is getting widespread these days epically in the health care insurance system, dealing with this problem has become a necessity. Unsupervised machine learning algorithms such as K-Means clustering along with supervised algorithms used in machine learning, like support vector machines, logistic regression, design trees etc. can play a very vital role in binary class classifications, which would ultimately help in identifying and outreaching the desired goal of fraudulent detection. In the end, this paper specifies the best or the most appropriate model that could be used in the given dataset to produce the most accurate results, based on certain parameters of confusion metrics like accuracy, precision, and specificity. 2023 IEEE. -
Analysis of Human Physiological Parameters Using Real-Time HRV Estimation from Acquired ECG Signals
The overall healthiness of the heart can be computed from Electrocardiogram. The healthiness of the heart depends on several lifestyle parameters, like as- stress, sleeping pattern, smoking habit etc. In this paper, an algorithm to determine Heart Rate Variability from the acquired ECG signal on a real-time basis is presented. Impacts of above-stated lifestyle parameters on cardiac health using Heart Rate Variability analysis are also computed. ECG signal gets contaminated with different sources of noises while acquisition. Multi-rate FIR Impulse Filter is used for de-noising of the acquired signal. Heart Rate Variability analysis and real-time plotting are done on de-noised output for accurate feature extraction. A simple robust hardware realizable algorithm was developed for analyzing obtained HRV to state different health conditions of the heart. 2019 IEEE. -
Analysis of impact investment for sustainable development in India
Impact investment is a form of investing the purpose of which is to generate not just financial returns, but positive social or environmental impacts also. The objective of this research is to observe the trends that are being followed in the Indian impact investing landscape and draw the comparisons and/or similarities with the data published by the Global Impact Investing Network (GIIN), a nonprofit organisation committed to expanding the scope of impact investing globally. For the objective of gauging the Indian impact investment movement, primary data was collected through a survey of 238 Indian impact investors, and the data collected was analysed descriptively and compared with secondary data available on the GIIN's website. The insights that are provided by this study shall aid investors as they are encouraged to review the implications for their own strategies and patterns of investing and investigate these actionable measures so as to facilitate a sustained responsible industry growth, improve market transparency, and reinforce the patterns of investment decision-making. 2024, IGI Global. All rights reserved. -
Analysis of imperfect interfaces in cobalt ferrite plates using a linear spring model: a comparative study with terfenol-D
Purpose: This research aims to explore the transmission of seismic surface waves through a two magneto-strictive materials i.e. cobalt ferrite and Terfenol-D when embedded in a plate-substrate configuration with non-ideal interface. The study focuses on understanding the impact of width of the plates, imperfect parameter, heterogeneity parameter on both the materials cobalt ferrite and Terfenol-D under magnetically open and short conditions. Methodology: To achieve this, the study employs a variable-separable technique following Direct Sturm-Liouville method and appropriate boundary conditions to derive frequency relations for both magnetically open and short circuit scenarios. Numerical simulations are conducted to investigate the effects of width of the plates, imperfect parameter, heterogeneity parameter on both the materials cobalt ferrite and Terfenol-D under magnetically open and short conditions. Findings: The research findings indicate that the phase velocity is increasing more in Terfenol-D as compared to Cobalt ferrite, either the case magnetically open or closed. Graphical comparisons highlight the impact of width plates, imperfect parameter, heterogeneity parameter on the characteristics on wave propagation clearly. Research limitations: The study is confined to linear wave propagation and does not consider nonlinear effects. Additionally, the analysis is based on idealized material properties and interface conditions. Practical implications: The results of this research can contribute to the design and optimization of sensors, energy harvesters, and wave manipulation devices utilizing piezomagnetic materials. Understanding the behaviour of surface waves in these structures is crucial for their effective application. Originality: This study offers a comprehensive analysis of surface wave propagation in two different types of piezomagnetic composite structure by considering heterogeneity and interface conditions. The comparative study of different piezomagnetic models and the incorporation of heterogeneity and interface conditions contribute to the originality of the research. The Author(s) 2024. -
Analysis of indias trade patterns and trade possibilities with the european union
Trade has played a crucial role in the emergence of developing econo-mies. The global emergence of India is also linked to its role in global trade. In this context, the European Union and India initiated talks for a free trade agreement known as the Bilateral Trade and Investment Agreement (BTIA). However, this agreement has failed to materialise due to various challenges and disputes. Against this backdrop, the present study attempts to trace the existing pattern of trade relations between India and the EU and provide a preliminary analysis of the nature of trade in this proposed region. A modified gravity equation and indicators of regional trade interdependence have been estimat-ed. The results indicate that trade within this region is in line with cer-tain predictions of the gravity model. Additionally, it also indicates that such an agreement has little potential for expanding trade and might even result in unnatural trade. Thus, it provides evidence for the argu-ment that India can benefit from developing ties with similar emerging economies in the Asia-Pacific neighbourhood. 2020, WSB University. All rights reserved. -
Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review
Ultrasonography is the most accepted and widely used imaging technique due to its non-invasive and radiation-free nature. The heterogeneous structure of kidney makes the disease detection a difficult task. Hence, more efficient models and methods are required to assist radiologists in making precise decisions. Since ultrasound imaging is considered to be the initial step in the diagnosis, more efficient processing techniques are needed in the interpretation of images. The presence of speckle noise is a challenge task in image processing. It diminishes the clarity of the images. In this article, an in-depth review has been performed on various machine learning and deep learning techniques, which are helping to improve the quality of images. The pre-processing, segmentation, feature extraction, and classification are described in detail using kidney cyst, stone, tumor, and normal kidney images. Deep learning techniques are enhancing the quality of the images with better accuracy. The remaining challenges and directions for future research are also explored. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of machining parameters for face milling of inconel 718 using response surface methodology
The machining of Inconel 718 which is a nickel based super alloy has become a material of great importance mainly in the aerospace industry. Reason being the materials possesses properties of increase in strength at elevated temperature, high resilience to chemical reaction and high wear resistance. Gaining optimum machining parameters have become a great concern in the manufacturing industry, where economy of machining plays a very important key role in the market. This paper gives an overview of the experimentation conducted on the basis of Response Surface Methodology (RSM). Regression equations have been developed for surface roughness, by taking into consideration the machining parameters like cutting speed, feed rate and depth of cut for face milling operation performed in CNC machine. RSM analysis was carried out with the help of Mini Tab 18 software. The Mathematical equation developed after regression analysis shows to be very efficient. BEIESP. -
Analysis of Market Behavior Using Popular Digital Design Technical Indicators and Neural Network
Forecasting the future price movements and the market trend with combinations of technical indicators and machine learning techniques has been a broad area of study and it is important to identify those models which produce results with accuracy. Technical analysis of stock movements considers the price and volume of stocks for prediction. Technical indicators such as Relative Strength Index (RSI), Stochastic Oscillator, Bollinger bands, and Moving Averages are used to find out the buy and sell signals along with the chart patterns which determine the price movements and trend of the market. In this article, the various technical indicator signals are considered as inputs and they are trained and tested through machine learning techniques to develop a model that predicts the movements accurately. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Membership Probability in Nearby Young Moving Groups with Gaia DR2
We analyze the membership probability of young stars belonging to nearby moving groups with Gaia DR2 data. The sample of 1429 stars was identified from "The Catalog of Suspected Nearby Young Moving Group Stars." Good-quality parallax and proper motion values were retrieved for 890 stars from the Gaia DR2 database. The analysis for membership probability is performed in the framework of the LACEwING algorithm. From the analysis it is confirmed that 279 stars do not belong to any of the known moving groups. We estimated the U, V, W space velocity values for 250 moving group members, which were found to be more accurate than previous values listed in the literature. The velocity ellipses of all the moving groups are well constrained within the "good box," a widely used criterion to identify moving group members. The age of moving group members are uniformly estimated from the analysis of the Gaia color-magnitude diagram with MIST isochrones. We found a spread in the age distribution of stars belonging to some moving groups, which needs to be understood from further studies. 2020. The American Astronomical Society. All rights reserved.. -
Analysis of MRI Images to Discover Brain Tumor Detection Using CNN and VGG-16
Brain tumor is a malignant illness where irregular cells, excess cells and uncontrollable cells are grown inside the brain. Now-a-days Image processing plays a main role in discovery of breast cancer, lung cancer and brain tumor in initial stage. In Image processing even the smallest part of tumor is sensed and can be cured in early stage for giving the suitable treatment. Bio-medical Image processing is a rising arena it consists of many types of imaging approaches like CT scans, X-Ray and MRI. Medical image processing may be the challenging and complex field which is rising nowadays. CNN is known as convolutional neural network it used for image recognition and that is exactly intended for progression pixel data. The performance of model is measured using two different datasets which is merged as one. In this paper two models are used CNN and VGG-16 and finding the best model using their accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of multilayer convective flow of a hybrid nanofluid in porous medium sandwiched between the layers of nanofluid
AgBr acts as a good sensitizer for titanium oxide, hence TiO2AgBr nanoparticles exhibit high photocatalytic activity which helps decompose methyl orange under visible light irradiation. Methyl orange is a chemical compound that is hard to degrade and has high stability. It is photoreactive and can capture photons from the sun and is highly used as a light harvester in solar cells, hence, it is used in solar applications. In view of this, the present article deals with the analysis of heat transfer in a multilayer flow of two immiscible nanofluids in a vertical channel that finds application in the fields of solar reactors, electronic cooling, and so on. The mathematical model involving the effect of thermal radiation and the presence of heat source is in the form of a system of ordinary differential equations. This system of equations is simplified using the differential transform method-Padapproximant and the resulting equations are solved algebraically. It is observed that the temperature of the coolant does not reach its saturation point faster due to the presence of different base fluids that differ in their thermal conductivity. This helps in maintaining theoptimum temperature of the system. 2021 Wiley Periodicals LLC -
Analysis of multimode oscillations caused by subsynchronous resonance on generator shaft
Series capacitors are installed in high voltage alternating current transmission lines to counteract the inductive reactance of the line. The resonance caused by series capacitors between electric system and mechanical system at frequencies less than the synchronous speed, leads to torsional oscillations. Undamped oscillations ma y cause a severe fatigue in the turbine generator shaft system. Rotating component undergoes various modes of oscillations when it is subjected to resonance. Rotor oscillate in different modes such as swing mode, super synchronous mode, electromechanical mode and torsional mode. Rotor dynamics of rotating structure depends on several factors like Coriolis Effect, moment of inertia and stiffness coefficient. Modal analysis using finite element method gives the natural frequency and mode shapes of any rotating structures. In this paper, a two mass rotating system which is analogous to turbine generator is subjected to resonance by adding series capacitors and its dynamic behavior is studied using finite element method. 2018 Lavoisier. -
Analysis of Multinomial Classification for Legal Document Categorization
A major area of research today is the application of Machine Learning Techniques for Document or Text Classification. Document Classification is an important aspect of Electronic Discovery in the Legal domain. The need for the process to be automated has been realized over the past few years. Multinomial Classification is a well-known Supervised Machine Learning Technique that helps us classify if there are more than two classes used for the purpose of Classification. Evaluation metrics such as Precision, Recall, and F1 Score have been used to measure the efficiency of Classification. Logistic Regression and Gradient Boosting Algorithms have outperformed other Multiclass Classification techniques. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance
Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifier (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Analysis of Nine Level Single-Phase Cascaded H-Bridge Inverters for EVs
This paper explores the design and operation of a Modular Nine-Level Inverter (MLI)-Electric Vehicle (EV) charging system, incorporating solar energy to power domestic loads and charge EVs. The system comprises a solar panel, DC-DC regulator, and MLI for efficient energy conversion. The MLI's modular design reduces complexity and enhances efficiency. Equivalent circuits illustrate voltage level generation, while PWM control regulates power device switching for precise output control. Performance metrics, including regulated DC supply voltage and staircase nine-level output voltage, demonstrate the system's capability for diverse applications. A nearly sinusoidal current waveform and harmonic analysis underscore the system's effectiveness in delivering stable power with reduced harmonic distortion. Comparisons between filtered and unfiltered output highlight the importance of filtering techniques in improving power quality. Overall, the MLI-EV charging system showcases advancements in renewable energy integration, offering a versatile solution for sustainable electricity generation and EV charging. 2024 IEEE. -
Analysis of nonlinear compartmental model using a reliable method
The goal of this work is to investigate nonlinear models and their complexity using techniques that are universal and have connections to historical and material aspects. Using the premise of a constant population that is uniformly mixed, a nonlinear compartmental model that depicts the movement between voter classes is taken into consideration. In the current work, we investigate the dynamical framework that supports the interactions between the three parties. It is discussed how rate change affects various metrics. The conditions for boundedness, stability, existence, and other dynamics are obtained. We derive the effects of generalizing the model in any order. The current study supports investigations into complex real-world issues and forecasts of necessary plans. 2023 The Author(s) -
Analysis of nonlinear convection and diffusion in viscoelastic fluid flow with variable thermal conductivity and thermal radiations
The study offers a thorough evaluation of the complex fluid dynamics and heat transfer phenomena in Williamson viscoelastic fluid flow, taking into account thermal radiations and variable thermal conductivity. The paper extends its analysis to include heat transfer effects, which are critical in several engineering and industrial applications, and digs into the complexity of non-Newtonian fluid behavior, with a special focus on thermal radiation, heat production, diffusion and viscous dissipation. The study makes use of mathematical models and numerical method RK4 to clarify the nonlinear interactions between convection and diffusion processes in this viscoelastic fluid. The energy and concentration equations are simulated in the presence of the modified Fourier and Fick laws. Moreover, the predicted heat flow is based on the Cattaneo-Christov theory. This research also sheds light on the interaction between rheological properties and thermal characteristics, providing important new knowledge to the broader field of fluid dynamics and heat transfer. 2024 World Scientific Publishing Company. -
Analysis of Online In-Destination Booking Service Processes in the Travel Industry: A Case Study
This article presents a comprehensive analysis of the online in-destination booking service processes within the dynamic landscape of the travel industry. Utilizing a case study approach, the research investigates the various stages involved in providing travel-related services, focusing on the key players. The study employs a quantitative method to assess the information quality, system quality, service quality, customer satisfaction, and purchase intention of online in-destination booking. The research highlights the investigation of the usability of online travel booking systems and identifies the purchase intention of customers towards online travel booking websites. To address the research objectives, the participants are selected using a nonprobability sampling method. The sample size of the study is 225 from in and around Coimbatore. The sampling procedure used is convenience sampling. The sampling is selected based on convenience and accessibility to the residents. The findings reveal that there exists a significant difference in respondents opinions on quality criteria: system quality and service quality. Additionally, the study finds that the loading time of online travel booking websites is positively correlated with quality criteria and features of travel apps. By examining a specific case within the travel sector, this study contributes valuable insights that can inform strategic decision-making for businesses operating in the online in-destination booking space. The results aim to guide industry players in enhancing their operational efficiency, leveraging technology advancements, and aligning their services with evolving customer expectations, ultimately fostering sustainable growth in the competitive travel market. 2024, Bentham Books imprint. -
Analysis of Perceptions and Attitudes of Scheduled Commercial Bank Personnel Toward Provision of Credit to Poor and Toward Financial Inclusion Process in India
Journal of Investment and Management, Vol-1 (1), pp. 1-11.