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A Space Vector Modulated Direct Torque Control of Induction Motor with Improved Transient Performance and Reduced Parameters Dependency
Direct torque control (DTC) of induction motors is hampered by high torque and current ripple. Integrating DTC with space vector pulse width modulation (DTC-SVPWM) is one of the frequently used approaches to solve this problem. However, it adds to the computational complexity, increases the number of necessary motor parameters needed for control scheme implementation, and also affects the transient performance of the induction motor; this approach compromises the robustness and simplicity of DTC scheme. To get around these restrictions, a novel control strategy is put forth in this paper. The suggested scheme enhances the steady-state performance and transient response of the motor while preserving the simplicity and robustness of the DTC scheme. To accomplish this, the proposed control scheme operates at varying switching frequencies during transient conditions and constant switching frequencies during steady-state. The suggested speed control method does not employ any rotating reference frame transformations or usage of many rotor parameters for computation, nor does it call for sector identification and operates with a single PI controller. The suggested topology also uses a bus-clamped PWM modulation technique, which lowers the average switching frequency to 2/3 times the actual switching frequency. Thus, switching losses are also decreased. Simulation results show the effectiveness of the proposed topology in enhancing the transient and steady-state performance of the induction motor. The results are compared with the traditional DTC and DTC-SVPWM scheme. 2023 IEEE. -
A Specular Reflection Removal Technique in Cervigrams
Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcopes. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several prominent approaches for removal of specular reflections proposes a novel method to remove the specular reflections. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. 2023 IEEE. -
A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data
Demand forecasting is crucial in the business sector. Despite the inherent uncertainty of the future, it is essential for any firm to be able to accurately predict the market for both short- and long-term planning in order to place itself in a profitable position. The proposed approach focus on the passenger transport sector because it is particularly vulnerable to fluctuations in consumer demand for perishable commodities. At every stage of the planning process from initial network designs to final pricing of inventory for each vehicle in a route-an accurate prediction of demand is essential. Forecasting passenger demand is crucial since passenger transportation is responsible for a substantial chunk of global commerce. The suggested method relies on three distinct techniques: data preparation, feature selection, and model training. Data modification, cleansing, and reduction are the three sub-processes that make up preprocessing. When it comes to feature selection, partition-based clustering algorithms like k-means are the norm. Let's go on to training the models with stacked BiLSTM. The proposed method is demonstrably superior to both LSTM and BiLSTM, the two most common competing approaches. The proposed method had a success rate of 98.45 percent. 2023 IEEE. -
A structured approach to implementing Robotic Process Automation in HR
Technological innovations are changing the industrial landscape. As technology transforms the world, the HR function needs to focus on embracing automation and other technologies that promise efficiency, service effectiveness and cost savings. Deployment of robotic process automation (RPA) can help (a) to offer better service to employees and managers (b) ensure compliance of HR processes with standards and regulations (c) facilitate rapid initiation and completion of HR processes (d) enhance efficiencies by digitizing data and auditing process data (e) improve HR productivity and cost savings by automating manual and repetitive tasks. A robust and structured approach needs to be in place to identify HR processes that can be automated using the RPA approach. In this paper the authors (a) suggest a four step approach - validation, assessment, evaluation and classification - to analyze processes and verify their suitability for automation using RPA (b) identify HR processes that has relevance for the RPA approach within the broad areas of HR Strategy, Talent Acquisition, Talent Development & Performance Management, Compensation & Benefits, HR Operations and Employee Relations (c) recommend a process for mapping HR RPA propensity. A case study is also presented for greater clarity on adoption of RPA in HR processes. Published under licence by IOP Publishing Ltd. -
A Structured Design of 5G Based Assisted MTC System using Mission-Critical System
Critical machine-type relationships (mc MTC) has become known as a crucial element within the Business Internet of Things (IoT) ecosystem, showcasing lucrative opportunities in disciplines like autonomous vehicles, intelligent energy/smart grid control, security services, while advanced wearable applications. As the fifth generation of cell phones unfolds, the changing environment of mc MTC puts diverse demands on the underlying technology. These demands embrace standards for low power usage, heightened dependability, and minimal delay connection. In answer to these challenges, recent versions and current advances in Long-Term Evolution (LTE networks) systems have added features that promote cost-effective solutions, increase coverage, reduce delay, and improve reliability for devices with different movement levels. This study focuses on assessing the impacts on mc MTC effectiveness in a connectivity network for 5G with varying user and equipment accessibility, influenced by a variety of movements. According to the study, integrating other modes of contact, such as quadcopter assistance and device-to-device linkages a voice, contributes a crucial role in achieving the strict demands of mc MTC programs across diverse situations that tell which includes industrial automation, vehicular connection, and urban messages. Significantly, our results confirm gains of as much as forty per cent in link availability and dependability when applying nearby connections as opposed. 2024 IEEE. -
A Study and Analysis on Various Types of Agricultural Drones and its Applications
Drones are considered to be the greatest invention of mankind. Drones can be used in many areas widely. Drones can also be used in agriculture and it is called as unnamed aerial vehicles (UAV). In the traditional agriculture methods land vehicles are used to monitor various activities of the agriculture, this was consuming lot of human effort and time. Using drones in agriculture is more beneficial than using traditional methods for the activities. Usage of drones in agriculture provides a huge benefit in terms of economy and time due to their most astonishing features. In recent years many surveys have proved that drones can cover almost 10 to 15 times of the area which can be covered with traditional land based techniques. Drones can be controlled by computers according to their capacities, that is drones can be automated over some range of area, locating remote area, and even can be semi-automated. Drones can be efficiently used in agriculture for performing certain activities such as, studying weather conditions and variations, infection for the crops, land fertility and many more. Because of the efficiency of the drones they can be used in various activities of agriculture. In this paper, a detailed study has been made on various types of agricultural drones based on the feature, capacity, range as well as cost and the area of agriculture where they suit the most, and a statistical analysis about the usage of the drones in the field of agriculture. 2020 IEEE. -
A Study Examining the Relationship Between College Students Demographic Characteristics and Financial Literacy- With Special Reference to a Union Territory in India
Over the past ten years, the importance of financial literacy has been growing across the world. Prior research has found that a lack of financial knowledge can have several negative consequences, the inability to make correct financial decisions, high levels of debt, high-cost borrowing and misuse of credit. Limited knowledge of financial concepts also has an impact on the economy as a whole. This study attempts to measure the level of financial literacy of college students in Goa, India. A total of 378 respondents were surveyed and their level of financial literacy was measured through a percentage analysis. The respondents level of financial literacy was also studied concerning various demographic characteristics. The results show an association between financial literacy and sex, level of education, field of education, percentage of respondents and income level. The findings of the study suggest a need for the strengthening of initiatives by policymakers to introduce the concept of financial literacy for students all over the state as well as the country, in every field. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A study of Autoregressive Model Using Time Series Analysis through Python
A Time-series investigation is a simple technique for dividing information from reconsideration perceptions on a solitary unit or individual at ordinary stretches over countless perceptions. Timeseries examination can be considered to be the model of longitudinal plans. The most widely used method is focused on a class of Auto-Regressive Moving Average (ARMA) models. ARMA models could examine various examination questions, including fundamental cycle analysis, intercession analysis, and long-term therapy impact analysis. The model ID process, the meanings of essential concepts, and the factual assessment of boundaries are all depicted as specialized components of ARMA models. To explain the models, Multiunit time-series plans, multivariate time-series analysis, the consideration of variables, and the study of examples of intra-individual contrasts across time are all ongoing improvements to ARMA demonstrating techniques. [1] 2022 IEEE. -
A study of CNN models for re-identification of vehicles
Vehicle Re-identification has evolved in recent times. Initially, clicking a single picture of a vehicle or a car was done manually, inviting the workforce to complete a specified task. With the growth in technology, the method and techniques in Vehicle Re-Id also have advanced, transforming from manual to automation. Surveillance cameras were used to capture vehicle images and retrieve information about a specific vehicle. Re-trieving and identifying the images of the vehicle is done using computer vision, the most important branch of computer science and artificial intelligence. Earlier, Vehicle Re-Id implemented a single algorithm on a dataset, making the corresponding result insufficient to determine its effects. This paper proposes a brief survey of multi-modal techniques and methods for vehicle re-identification and fingerprinting. The different attributes of the vehicle are considered for ANPR (Automatic number plate recognition) for identifying the number plate, focusing on the vehicle's details or features as the initial phase of identification, and then the vehicle number plate. 2023 IEEE. -
A Study of Emotion Classification of Music Lyrics using LSTM Networks
Emotion Recognition is a vital component of human-computer interaction and plays a pivotal role in applications such as sentiment analysis, virtual assistants, and affective computing. Long Short-Term Memory (LSTM) models are a subset of Recurrent Neural Networks (RNNs). It has gained significant popularity for their effectiveness in sequence modeling tasks, including emotion recognition. The study presents a review on the application of Long Short-Term Memory (LSTM) networks for emotion classification using music lyrics. It offers a thorough review of relevant literature and outlines the methodology for implementing LSTM models for emotion recognition. Furthermore, the study emphasizes the significance of hyperparameter tuning in building effective machine-learning models, particularly LSTM-based models. 2024 IEEE. -
A Study of Factors Affecting the Adoption of Digital Currencies
Digital currency has taken into the world slowly but steadily, rising in the leads of trades and commercialization, which can create a huge impact on the economic wellbeing. Digital currencies can be further classified into Cryptocurrencies, Virtual currencies and Central bank digital currencies. In this research we study thefactors of adopting digital currencies. Primary data has been collected using structured questionnaire. A total of 140 responses are used for the purpose of analysis. We have used correlation and heatmap foranalysing the impact of the identified factors such as Technological, Economical and Social. 2024 IEEE. -
A Study of Financialization of Commodity Markets in India
For numerous financial institutions, Commodity Futures (CF) has emerged as a widespread asset class since the 2000s. From 2000 to 2010, the estimation of the number of commodity index traders quadrupled, also, the number of hedge funds tripled. Recently, it has been noticed that in India, there occurs a vast inflow of investment toward the CF. Simultaneously, there occurs a problem of extremely higher prices along with volatility in commodity prices in India. However, studies on the financialization of the Commodity Market (CM) in India are not sufficient. This study was presented for analyzing the role of the financialization of CMs in India. Analyzing the association betweenCMs and equities markets in India is the major intention behind this study. Here, the Indian- MCX of India, the NSE of India, and the S&P500 Index are the sources from where the data has been gleaned. The outcome has been evaluated by utilizing a vector autoregression. The output demonstrated that no positive interdependence was exhibited by the correlation betwixt MCX Comdex returns and CNX Nifty. Consequently, a higher percentage of the mean value was attained by the commodity of daily returns of metal of commodity of agriculture. 2023 EDP Sciences. All rights reserved. -
A Study of Investment Behavior Of Economically Weaker Section (EWS) Investors
While investing, it is most important for an investor that he/she understand and follows the basic principles of investing to gain maximum advantages out of it. The present study analyzes the investment behavior of 190 economically weaker section (EWS) investors and rank their preferences and reasons using Garret ranking. The study observes that investors prefer to invest in traditional investment avenue over modern avenue due to lack of awareness and ease of investing across demographics. Results of ANOVA inform a small shift to mutual funds and change in perceived risk and return behavior in selected age, income and education category. The study recommends for opening of dedicated small financial planning centers/branches/kiosks etc to increase their awareness level and participation so that they can gain maximum advantages from their investment. The Electrochemical Society -
A Study of Preprocessing Techniques on Digital Microscopic Blood Smear Images to Detect Leukemia
Digital microscopic blood smear images can get distorted due to the noise as a result of excessive staining during slide preparation or external factors during the acquisition of images. Noise in the image can affect the output of further steps in image processing and can have an impact on the accuracy of results. Hence, it is always better to denoise the image before feeding it to the automatic diagnostic system. There are many noise reduction filters available; the selection of the best filter is also very important. This paper presents a comparative study of some common spatial filters like wiener filter, bilateral filter, Gaussian filter, median filter and mean filter which are efficient in noise reduction, along with their summary and experimental results. Performing comparative analysis of result based on PSNR, SNR and MSE values, it can be determined that median filter is most suitable method for denoising digital blood smear images. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Study of Segmentation Techniques to Detect Leukaemia in Microscopic Blood Smear Images
In medical image processing, the segmentation of the image is considered to be a vital stage and is effectively used to extract the region of interest. Automated diagnosis of leukaemia is highly associated with the accurate segmentation of the cell nucleus. The purpose of this paper is to review and analyze literature related to some of the major segmentation techniques used in the field of Acute lymphoblastic leukaemia (ALL) detection. This paper presents an overview of segmentation methods along with the experimental results of six implemented methods and highlights some of the advantages and disadvantages of implemented segmentation techniques. 2020 IEEE. -
A Study of Simulated Working of A* and RRT* for Cargo Ship in ASVs
With the increased amount of algorithms for the path planning and collision avoidance of ASVs. The need for an unbiased protective path planning directs the need for decision in stochastic areas in the vast ocean for cargo ship. Autonomous surface vehicles should take appropriate decision on the path according to the dynamic environment and the obstacle that is before them. In some cases, environment, time, and size should be considered to acquire the fastest path and methods that could be suited for collision avoidance. This paper investigates the need for a well-known path planning method that has handled the situation based on the dynamic properties of the vehicle in the ocean. The simulated result shows a slight variation in their proposed path in terms of time and collision in terms of size. Therefore, using a realistic approach of the A* algorithm and the RRT*, we can handle the scenario of dynamic environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Study of the Influence of Investor Sentiment based on News and Event on the Cryptocurrency Market during Russia Ukraine War
As a new and emerging digital asset, Cryptocurrency has been traded for more than a decade, reaching a very high market capitalization and continuing to increase its volume of trading at a very rapid pace. Many countries have legalized or are considering legalizing cryptocurrency as a trading platform for this asset, and many companies worldwide accept it as a medium of exchange. As a result of this expansion, many researches in finance literature have focused on studying the efficiency of this cryptocurrency market. In line with this literature, this paper examines, using the abnormal returns and abnormal trading volumes methodologies, the dynamics of investors' reaction to the arrival of unexpected information like The Russia Ukraine War regarding the Cryptocurrency market in the context of the two hypotheses: the uncertain information and the efficient market hypotheses. 2024 IEEE. -
A Study on Challenges and Solutions in the Uptake of Agricultural Technology Startups Services in Karnataka
In congruence with overarching trend of digitalization sweeping across India, agricultural sector is currently experiencing remarkable advancements propelled by innovative technological solutions introduced by emerging startups in agritech domain. The state of Karnataka is swiftly solidifying its position as preeminent leader in agritech industry attracting heightened interest from venture capital investors in recent times and emerging as dominant recipient of these investments garnering substantial 52% share followed by Maharashtra at 18% and Tamil Nadu at 9.2%. The principal aim of this research endeavor is to scrutinize socioeconomic impediments hindering adoption of AgriTech within rural precincts of Karnataka specifically in districts of Rural Bangalore (Doddabalapura and Nelmangala) and Davanagere (Shiramagondonahalli). The study seeks to gauge perceptions of farmers regarding potential solutions aimed at fostering greater adoption of AgriTech in these aforementioned regions. The study employed descriptive analysis by utilizing data obtained from judiciously selected sample of 120 farmers dichotomized into those who had availed themselves of AgriTech services and those who had not as provided by AgriTech firms. Empirical findings illuminate formidable impact of socioeconomic factors encompassing economic standing, land ownership classification and educational attainment in shaping farmers receptivity toward AgriTech utilization. The study unearthed valuable insights pertaining to propositions put forth by farmers to enhance adoption of AgriTech practices among their peers. The study furnishes valuable elucidations concerning barriers impeding adoption of AgriTech and offers viable solutions to invigorate increased participation among farmers in realm of AgriTech proffering pertinent recommendations to stakeholders such as AgriTech startup executives, researchers and policymakers urging them to meticulously assess local socioeconomic dynamics and tailor AgriTech services in accordance with discerned needs and preferences of farming community. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Study on Crude Oil Price Forecasting Using RNN Model
Crude oil forecasting plays an important role in every countrys economic progress. Inflation is likely to rise as oil prices rise, delaying economic progress. In terms of inflation, oil prices directly affect the expense of commodities produced using petroleum products. Not only crude, this paper provides the idea of best prediction models that could be used for easy prediction in stocks. It provides an overview of the data and methodology. As a result, we have compiled a list of articles that discuss the impact of crude oil on various stock markets and how it affects different countries. And in general, we were looking for the optimal price prediction model between gated recurrent units (GRUs) and long short-term memory (LSTM). 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A study on effect of branding on customer buying behaviour with reference to Vellore
We set out to discover how consumers really feel about different branded items by conducting this study. This research aims to examine what variables impact consumers when they buy a product. There are a lot of aspects that affect a product's brand value these days, but consumers are especially sensitive to the product's reputation when making purchases. Another important factor in how people perceive a brand is advertisements. The study was conducted with a sample size of 50 and was confined to the Chennai area. All of the tests performed here made use of SPSS statistical software, and the data used is primary data. 2024 AIP Publishing LLC.