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Progression of Metamaterial for Microstrip Antenna Applications: A Review
This article provides an overview of the evolution of metamaterials (MTM) and all the aspects related to metamaterial development for antenna applications. It will be a useful collection of information for antenna researchers working in metamaterials applications. It gives an insight into the various metamaterial structures utilized along with miniature antenna designs. Different types of design parameters studied by the previous researchers are showcased to understand better perception of the metamaterial usage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
New Paradigm of Marketing-Financial Integration Modelling for Business Performance: An IMC Model
When it comes to the provision of financial services, the integrated marketing communication (IMC) process is crucial in the creation and maintenance of client-provider bonds. This research presents a literature assessment on the theoretical basis for using marketing communication tools in the provision of financial services. This research is an attempt to bolster the little theoretical literature on the effectiveness of marketing communication techniques in the provision of financial services. Financial service providers use marketing communication as a channel for two-way exchanges with their clientele, with the ultimate goal of maximising the benefits their customers bring to the company. When it comes to providing financial services, an organisations success hinges on its ability to effectively manage its relationships with both current and potential consumers. As a result, it is important for practical reasons to be guided by well-defined marketing communications goals to identify the extent of usage and within the constraints of available resources. In this regard, businesses are free to establish specific communications objectives in accordance with their unique situations to direct the implementation of their IMC plan. This study aims to find out an impact of financial integration with IMC on business performance. This study is descriptive in nature. Primary data is collected with the help of questionnaire. The study finds that the financial integration in the IMC model has a statistically significant impact on business success. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Cricket Shot Classification with Deep Learning: Insights for Coaching and Spectator Experience Enhancement
The cricket field has undergone significant transformations owing to recent technological advancements, particularly in countries like India. Technology has been used to determine projected scores, chances of winning, run rates, and many more parameters. This study centers on employing Deep Learning in cricket, focusing on the classification of different types of shots played by batsmen to aid in creating coaching strategies and enhancing the spectator experience. The proposed model uses a dataset of cricketing shots generated by collecting images from the internet, comprising 5781 images of 7 distinct shot types played by batters. The VGG-16, VGG-19, and RestNet-50 model architectures were trained for the classification task, with the best result obtained from VGG-16. Pre-processing tasks, such as scaling, augmentation, etc., were performed on the images before classification. Subsequently, 85% of the total images were used to train the model and for testing, rest 15% of images, resulting in an accuracy of 96.50% from VGG-16, 92% from VGG-19, and 78% from RestNet-50. 2024 IEEE. -
Sub-Optimization based Random Forest Algorithm for Accurate and Efficient Land use and Land Cover Classification using Landsat Time Series Data
The land use and land cover (LULC) play an essential role to investigate the impacts of environmental factors and socio-economic development in the Earth's surface. Extracting the hidden information from the remote sensing images in the observed earth environment is the challenging process. In this research, implemented a model that uses Landsat data to investigate the LULC changes. Utilized the Landsat 5,7 and 8 as inputs for the 1985 to 2019 by Google Earth Engine (GEE) is applied for the robust classification. This paper proposed a Sub-forest optimization based Random forest (SO-RF) classifier with faster diagnosis speed for LULC classification. Moreover, to increase the multispectral Landsat band's resolution from 30 m to 15 m, the pan-sharpening algorithm is utilized. In addition, analyzed the various image configurations grounded numerous spectral indices and other supplementary data such as land surface temperature (LST) and digital elevation model (DEM) on final classification accuracy. The proposed SO-RF produced higher accuracy (0.97 for kappa, 96.78% Overall accuracy (OA), 0.94 for f1-score) than Copernicus Global Land Cover Layers (CGLCL) map and state of art methods like K-Nearest Neighbor (KNN), Decision Tree (DT), and Multi-class Support Vector machine (MSVM). 2024 IEEE. -
Cardless Society: Assessing the Role of Cardless ATMs in Shaping the Future of Financial Transactions
The ubiquitous ATM faces a critical crossroads in a world where the digital pulse is becoming more and more ingrained. The sound of plastic clicking, which used to be a comforting symbol of financial independence, is becoming less audible in the background noise of near-field communication and the Erie silence of digital scans. This study goes beyond the physical card and explores the unexplored world of cardless ATM technology, where security, convenience meet and innovation completely reimagines the process of getting cash. The meticulous analysis and potential use of technology can completely twist the dynamic rhythm of this world. 2024 IEEE. -
Optimizing Food Production with a Sustainable Lens: Exploring Blockchain Technology in Raw Plant Materials and Organic Techniques in Achieving Sustainable Development Goals
Amidst a rising population and mounting environ- mental concerns, India seeks a transformative approach to ensure food security and sustainable agriculture by 2030, as outlined in Sustainable Development Goal 2 (SDG 2). This research explores the immense potential of organic farming methods and raw plant materials to unlock this vision. Plants have a wealth of unrealized potential that extends beyond their conventional functions. The study looks at how different plant parts, like branches, leaves, stems, and even "waste"materials, can be used in a variety of ways to increase self-sufficiency, lessen environmental impact, and access renewable resources. Case studies from across the globe highlight this potential, highlighting the many advantages for the environment and communities. Additionally, the study investigates the innovative use of blockchain technology to promote a more transparent and resilient agricultural environment in India. Imagine blockchain-powered climate-smart practices, safe and transparent transactions, and precision agriculture led by sensor data. Water-efficient irrigation, environmentally friendly pest control, and strong traceability systems are all part of this vision, which aims to strengthen the Indian agricultural sector's resilience. The study suggests a framework of customized policy recommendations centered on non-losable farming methods in recognition of the need for wider implementation. This framework, created especially for the Indian context, supports the promotion of agrotourism, improved education and extension services, accessible financial risk management tools, and the smart redistribution of subsidies. The research highlights the transformative potential of this approach by highlighting the many benefits of these practices, including the environmental (less water use, increased biodiversity, improved soil health, and carbon sequestration), social (better community resilience, food security, farmer income, preservation of cultural heritage, equitable trade), and economic (premium market access, lower input costs, and higher yields) gains. In the end, this research offers a strong plan of action for India to greatly advance SDG 2 and create a more sustainable future for all of its people. A food system that feeds people and the environment can be developed by carefully using organic farming methods and unprocessed plant resources in conjunction with successful legislative initiatives. 2024 IEEE. -
Application of smart manufacturing in business
The application of machine learning to production is becoming a chief objective for businesses all around the world. Smart product-service systems enable digital business model innovation by merging digitized product and service components. The life cycle that comes with the realization of customer value is a critical component of these industrial solutions and manufacturing industry is undergoing significant changes as a result of digitalization and automation. As a result, smart services, or digital services that generate value from product data, are gaining popularity. Customers may now contribute in greater numbers in product design during the design process. Giving more people access, on the other hand, increases the security vulnerabilities associated with cloud manufacturing. Smart Manufacturing is one of the technology-driven approach to manufacturing that uses network-connected machines to monitor the process. Smart manufacturing has the ability to be used in a variety of ways, including putting sensors in manufacturing machines and collecting data on their operating state and performance. Thus, the main purpose here is to find ways to improve and automate production performance. This conceptual paper attempts to give a view of how a smart intelligence system may be used in business and how individuals and organizations can produce value. 2023 Author(s). -
Global and Indian Perspectives on Russia-Ukraine War using Sentiment Analysis
In today's world, social media has become a platform through which people express their opinions and thoughts regarding various topics. Twitter is one such platform wherein people resort to expressing their opinions or portraying sentiments to the world. Today it has become easier to analyze mass opinion by using sentiment analysis. This paper investigates the ongoing Russia-Ukraine war by analyzing opinionated tweets, and it seeks to understand the sentiments from a global and Indian perspective. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. Multinomial Naive Bayes classifier classified the tweets into positive, neutral, and negative categories. The paper employed NRCLex for emotion classification and aspect-based sentiment analysis to divide opinions into aspects and determine the sentiment associated with each element. For the study, 4,31,857 tweets were extracted, and the results of sentiment analysis depict that 44.09% users had negative sentiments followed by 33.378% users expressing positive sentiment and remaining 22.53% people were neutral in their tweets. Fear, anger and sadness were amongst the top emotions expressed in the negative tweets whereas the positive tweets expressed trust and anticipation that the war would end soon. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. An analysis was performed on 1542 tweets that were obtained for Operation Ganga. 74.5% of the users had positive sentiments about Operation Ganga, whereas 16.67% and 8.5% had negative and neutral sentiments respectively. The people trusted this evacuation process resulting in more positive sentiments. Fear of losing near and dear ones and fear of safety was the topmost concern for Indians and leadership was one of the topmost aspects tweeted in the positive sentiments. Thus, the overall results depict that the common man does not prefer war and is fearful of the outcomes. The government should hear the voice of the common man and plan strategies and decisions considering the common man's sentiments. 2022 ACM. -
A Review on Rural Womens Entrepreneurship Using Machine Learning Models
Rural womens entrepreneurship has contributed significantly to the countrys economy. Entrepreneurship rates have fluctuated in recent years, according to a variety of reasons including economic, social, and cultural influences. Therefore, machine learning models are used to assess the features to make better business decisions. In this research paper, papers from 2009 to 2022 were studied and found that machine learning models are being used to improve womens entrepreneurship. In this paper, nine machine learning models have been described in detail which include multiple regression, lasso regression, logistic regression, decision tree, Naive Bayes, clustering, classification, deep learning, artificial neural network, etc. In the study of all these models, it was found how accurately this model has been used in womens entrepreneurship work. It has been observed that by using different machine learning models with the data acquired from rural entrepreneurship, women entrepreneurs may use a new way of understanding the dynamics of rural entrepreneurship. Various machine learning models have been studied to improve rural development for women working in rural areas. Thus, we have proposed a comparative study of various machine learning models to predict entrepreneurship-based data. The findings of this study may be used to assess how rural women entrepreneurs may change the decisions made in several domains, such as making use of different economic policies and promoting the long-term viability of women entrepreneurs for the countrys economic growth. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Efficient Power Conversion in Single-Phase Grid-Connected PV Systems through a Nine-Level Inverter
In this paper, a novel nine-level inverter-based method for achieving efficient power conversion in single-phase grid-connected photovoltaic (PV) systems is proposed. The traditional two-level inverter has poor power quality and a high harmonic content. By using fewer power switches and adding more voltage levels, the proposed nine-level inverter gets around these restrictions, improving power conversion efficiency and lowering total harmonic distortion (THD). The effectiveness of the indicated technique for accomplishing better power quality and greater overall system efficiency is demonstrated by the simulation findings. A promising approach to improving the efficiency of single-phase grid-connected PV systems is the suggested nine-level inverter. 2023 IEEE. -
Efficient Integration of Photovoltaic Cells with Multiport Converter for Enhanced Energy Harvesting
This research work presents a novel approach for the efficient integration of photovoltaic (PV) cells with a multiport converter to enhance energy harvesting in renewable energy systems. The proposed system combines the advantages of PV technology with the flexibility and scalability of multiport converters, enabling improved power extraction and utilization from solar energy sources. The integration is achieved by employing a multi-input multi-output (MIMO) control strategy, which optimally distributes power among multiple energy storage systems and loads. A comprehensive modeling and analysis of the PV cell characteristics and the multiport converter are conducted to identify the optimal operating conditions. Furthermore, a power management algorithm is developed to dynamically regulate the power flow and maximize the energy harvesting efficiency. The proposed approach demonstrates superior performance compared to traditional single-input single-output converters, achieving higher energy yields and enabling effective integration of PV cells in diverse applications. Simulation results validate the effectiveness of the proposed approach, showcasing its potential to significantly enhance energy harvesting from photovoltaic sources and contribute to the development of sustainable and reliable renewable energy systems. 2023 IEEE. -
Enhanced Design and Performance Analysis of a Seven-Level Multilevel Inverter for High-Power Applications
The structure and performance analysis of a seven-level multilevel inverter is discussed in this study. Due to their capacity to get around the drawbacks of traditional two-level inverters, like high voltage stress on power devices and harmonic distortion, multilevel inverters have attracted a lot of attention lately. Multiple voltage levels can be produced by the seven-level multilevel inverter which is being proposed because it uses a sequential arrangement of power sources and capacitors. The design methodology involves selecting appropriate power devices and capacitance values to achieve the desired voltage levels while minimizing losses and ensuring reliable operation. Total harmonic distortion (THD), inverter efficiency, and voltage stress on power devices are all considered as part of the performance analysis. In comparison to conventional two-level inverters, simulation results indicate that the proposed seven-level multilevel inverter offers lower THD, increased efficiency, and reduced voltage stress. This research contributes to the advancement of multilevel inverter technology and its potential applications in various power conversion systems. 2023 IEEE. -
Strategic Power Factor Management for Elevated Lift and Hoist Performance
The paper outlines the design and simulation of active power factor correction for a 100 hp induction motor using MATLAB/Simulink. In this system, the induction motor functions as the primary load, operating with a low power factor. Different load scenarios are simulated to examine the motor's performance. The current drawn from the supply is verified under varying conditions, both with and without the implementation of a variable capacitance bank. The power system network comprises apparatus such as Induction Motors, Power Transformers, and Induction Furnaces, contributing to a low power factor. The resultant low power factor leads to elevated energy consumption. To mitigate this, power factor correction is imperative. Utilizing a variable capacitance proves instrumental in enhancing the power factor. The capacitor compensates for a portion of the reactive power, consequently reducing the total reactive power drawn from the source. This reduction in reactive power contributes to an overall decrease in power consumption. The research focus is on the effective correction of the power factor for a 100 hp induction motor through comprehensive design and simulation using MATLAB/Simulink, providing valuable insights into the impact of variable capacitance on current draw under diverse load conditions. 2024 IEEE. -
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. -
Analyzing Dual-Stage Inverter Performance for Solar Grid Integration
This paper presents a comprehensive analysis of the performance of dual-stage inverters in the context of solar grid integration through simulation. Dual-stage inverters are increasingly recognized for their potential to enhance the efficiency and reliability of solar power systems by mitigating grid disturbances and optimizing energy extraction. Through detailed simulation studies, this research evaluates key performance metrics such as grid stability, power quality, and energy conversion efficiency. The simulation environment enables the exploration of various operational scenarios and system configurations to assess the versatility and robustness of dual-stage inverter solutions. Furthermore, the study investigates the impact of control strategies and parameter variations on the overall performance of dual-stage inverters, providing valuable insights for system optimization and design. 2024 IEEE. -
A Performance Investigations of Modular Multilevel Inverter with Reduced Switch Count
A multilevel inverter is a special variant of converter for dc-Ac conversion in medium and high voltage and power requirements. In this paper, a novel configuration with fewer switches needed has been developed for the staircase output voltage levels. Two direct current voltage sources and eight transistors are required to synthesize five levels across the load using the conventional topology. The modular topology has two dc voltage sources, and six switches with a five-level output. Using the optimum multi-carrier pulse width modulation approach, the voltage quality is enhanced and total harmonic distortion is reduced. Furthermore, the viability of the proposed topology in contrast to the conventional cascaded H-bridged multilevel inverter with five levels is established by presenting comparable results showing reduced power losses with varied modulation indexes and increased efficiency. The simulation analysis has been carried out using the MATLAB/SIMULINK tool. 2022 IEEE. -
Performance optimization for extraction, transformation, loading and reporting of data
Enterprise Resource Planning has become the cornerstone for making data acquisition and related operations more efficient. Recent advances in hardware and software technologies have enabled us to think about performance optimization. Ninety percent of ERP projects spend more than their allocated budgets and have exceeded the time schedule for implementation. There are many factors that can be attributed to the low success rate of implementation but one main factor is the performance of the ERP package itself. In this paper, we have described the Business Intelligence tool and database which is related to Systems, Applications and Products. It is popularly known as SAP. Based on this, a new, mulch-dimensional performance metric is proposed for extracting, transforming, loading and reporting the data. 2015 IEEE. -
Photon, Electron, Proton and Alpha Particle Interaction Parameters of Different Clays
Modern life has made human beings and nature vulnerable to harmful radiations at different levels. This can be a great health hazard of our times. Since there is no probability of dodging the harmful influence, the practical way out is having protective shielding. Lead, the most efficient attenuator in current use has the drawbacks of being heavy, toxic and capable of producing secondary radiations. Other attenuators concrete, glass etc. have similar deficits in use. This is the context of the scientific world's quest for a perfect shielding material which can provide protection from harmful radiations effectively, economically and environment friendly. This work attempts a computational study on the radiation shielding efficiency of different types of clays, understanding of which would enable its applications for radiation shielding. The presence of high Z elements and the layered structure of clay along with its good thermal stability make it ideal filler for an effective radiation shield. In this work, we have performed a systematic study of the mass attenuation coefficients, effective atomic number and electron density of various clay samples. 2022 American Institute of Physics Inc.. All rights reserved. -
Quality and Security Assurance Workload Scheduling in Heterogeneous Cloud Environment
The adoption of cloud computing has transformed how businesses manage their workloads, offering flexibility and efficiency. This study introduces a novel model that leverages trust mechanisms to ensure secure workload execution within heterogeneous cloud environments. The primary objective of this research was to enhance efficiency by reducing both time and energy consumption associated with executing workloads. The proposed model's efficacy was assessed through the examination of Montage and Inspiral workloads. The evaluation encompassed two smaller tasks from both Montage and Inspiral workloads, in addition to one larger task. To gauge performance, a comparative analysis was conducted between the proposed model and established models such as Energy Minimized Scheduling (EMS), Efficient Replanning (ERP), and Evolutionary Computing Workload Scheduling (EC-WSC). The findings reveal that the proposed model outperforms the existing models in terms of mitigating both time and energy expenditure for the considered workloads. 2023 IEEE. -
Deep Insights into 3D Face Reconstruction from Blurred 2D Inputs: A Comprehensive Framework
This framework outlines a multi-stage methodology for 3D face reconstruction driven by advancements in deep learning. The process involves image preprocessing with deblurring techniques and subsequent feature extraction using CNNs alongside traditional methods. Deep learning adapts to diverse image challenges, ensuring accuracy in 3D reconstructions. In medical imaging, the proficiency of 3D CNNs and GANs shines in extracting structures from MRI and CT scans. Post-processing steps encompass mesh smoothing and texture mapping for enhanced visual quality. Evaluation metrics (MAE, RMSE, IoU) guarantee the precision of depth estimations. Applications of deep learning span across CNNs, 3DMM, GANs, and networks for landmark detection and dense correspondence. Challenges include optimizing eye reconstruction, expanding applications, and addressing concerns related to data quality, privacy, and hardware requirements. 2024 IEEE.