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Gain and bandwidth enhancement by optimizing four elements corporate-fed microstrip array for 2.4GHz applications
This paper presents the performance analysis of an optimized corporate-fed Rectangular Microstrip Antenna Array of four elements and Rectangular Microstrip Antenna array with Semi-Circular Tabs on the nonradiating edges of each element of the array to operate at 2.4 GHz, with detailed steps of the design process. The proposed antenna structures have been designed using FR4 dielectric substrate having a permittivity ?r of 4.4 with a thickness of 1.6 mm. The simulations have been carried out by using Antenna simulator HFSS version 15.0.0 and performance was analyzed for gain, bandwidth, VSWR, return loss and radiation pattern. The gain of these simulated antenna arrays is 2.4381 dB, 8.2684 dB and 8.5621 dB with a return loss of ?22.4123 dB, ?14.1095 dB and ?15.7621 dB for Single-Element patch, conventional Rectangular Microstrip array and Rectangular Microstrip Antenna array with semicircular tabs respectively at 2.4 GHz. Bandwidths exhibited by Single-Element patch, RMSACT and RMSA are 59.8 MHz, 83.9 MHz, and 212.7 MHz, respectively. 2020, Springer Nature Singapore Pte Ltd. -
FUZZY SEMI-ESSENTIAL SUBMODULES AND FUZZY SEMI-CLOSED SUBMODULES
In this paper, we prove some properties of fuzzy semi-essential submodules and fuzzy semi-closed submodules I??k University, Department of Mathematics, 2023; all rights reserved -
Fuzzy Rule-Based Multimodal Health Monitoring System Leveraging Machine Learning Techniques Using Eeg Datasets For Human Emotion And Psychological Disorders
In recent decades, machine learning and data analysis have become increasingly important in mental health for diagnosing and treating psychological disorders. One area of particular interest is the use of electroencephalography (EEG) brainwave data to classify emotional states and predict psychological disorders. This study proposed a data fusion to enhance the precision of emotion recognition. A feature selection strategy using data fusion techniques was implemented, along with a multi-layer Stacking Classifier combining various algorithms such as support vector classifier, Random Forest, multilayer perceptron, and Nu-support vector classifiers. Features were selected based on Linear Regression-based correlation coefficient scores, resulting in a dataset with 39% of the original 2548 features. This framework achieved a high precision of 98.75% in identifying emotions. The study also focused on negative emotional states for recognizing psychological disorders. A Genetic Algorithm (GA) was used for feature selection, and k-means clustering organized the data. The dataset included 707 trials and 2542 unlabeled features. Resampling techniques ensured a balanced representation of emotional states, and GASearchCV optimized Gradient Boosting classifier hyperparameters. The Elbow Method determined the optimal number of k-Means clusters, and resampling addressed class imbalance. GA parameters and gradient- boosting hyperparameters were empirically determined. ROC curves and classification reports evaluated performance, resulting in a high accuracy of 97.21% in predicting psychological disorders. The proposed system employed fuzzy logic to calculate a health score that combines the outputs of the emotional and psychological disorder monitoring models for a multimodal health monitoring system. This approach provides a more comprehensive assessment of an individual's overall mental health status. The findings suggest that the system achieved high efficiency in predicting emotions, showcasing comprehensive progress in EEG-based emotion analysis and disorder diagnosis. These advancements have potential implications for mental health monitoring and treatment, particularly with the integration of the PHQ-9 Scale and fuzzy logic. -
FUZZY MODULARITY AND FUZZY COMPLEMENTS IN FUZZY LATTICES
In this paper, we study the concept of fuzzy modularity in fuzzy lattices. We also define a fuzzy Birkhoff lattice and study fuzzy complements in fuzzy lattices. We prove that the notions of a right and a left complement coincide in a fuzzy lattice I??k University, Department of Mathematics, 2022; all rights reserved -
Fuzzy logic based system of intelligent electric solar dyer for fruits /
Patent Number: 201941032820, Applicant: S Vairachilai.
The present invention is related to a system of intelligent electric solar dyer for fruits using fuzzy logic based algorithm processed by at least one processor of a central processing unit of the system. -
Fuzzy logic based system of intelligent electric solar dyer for fruits /
"Patent Number: 201941032820, Applicant: S Vairachilai.
The present invention is related to a system of intelligent electric solar dyer for fruits using fuzzy logic based algorithm processed by at least one processor of a central processing unit of the system. The system comprises a photovoltaic system to produce solar radiation system, & an electric generator system, used for direct supply of the electric to the system. The central processing units perform the dying function according to the type of the fruits and with efficient use of the solar energy and electric energy." -
Fuzzy Logic Based Energy Storage Management for Parallel Hybrid Electric Vehicle
For the parallel hybrid electric vehicle, the various control strategies for energy management are illustrated with the implementation of fuzzy logic. The controller is designed and simulated in two modes for the economy and fuel optimisation. In order to manage the energy in HEV with three separate energy sources - batteries, Fuel cell and a supercapacitor system, - this article intends to create a fuzzy logic controller. By considering a complete system, the operating efficiency of the components need to be optimized. the control strategy implementation will be performed by the forward-facing approach. The fuel economy is optimised by maximising the operating efficiency in this strategy while other strategies does not have this extra aspect. The ability controller for parallel hybrid vehicles is mentioned in this research to enhance fuel economy. Although the earlier installed power controllers optimise operation, they do not fully utilise the capabilities. Hybrid vehicles can be equipped with a variety of power and energy sources such as batteries, internal combustion engines, fuel cell systems, supercapacitor systems or flywheel systems. The Authors, published by EDP Sciences, 2024. -
Fuzzy based Controller for Bi-Directional Power Flow Regulation for Integration of Electric Vehicles to PV based DC Micro-Grid
Utilization of Electric Vehicle as an auxiliary power source to a DC micro-grid for active power regulation is examined here. This paper focus on development of a Fuzzy based controller capable of regulating the bi-directional active power flow between a 10 kW DC Micro-grid and an Electric Vehicle. The system enables to balance the load on grid by performing peak shaving during peak hours and valley filling during off-peak hours. The load curve of Bangalore city for a typical day was taken as the reference and was used to implement the power flow control. The DC grid was designed for a 10 kW PV based micro-grid. The integrated DC micro-grid was simulated on MATLAB/Simulink platform and the obtained characteristics demonstrate that the power flow from grid to vehicle and vehicle to grid during the peak and off-peak periods respectively. The auxiliary battery pack was stressed only to 10.7 % of its 1C-rating leaving scopes for higher level power transmission possible between the systems. 2019 IEEE. -
Future Technology and Labour - Are we Heading Towards a Jobless Future?
Technological innovations and the invention of machines powered by Artificial intelligence2have changed the way we work, interact and carry on our everyday lives. Automation wave has revolutionized the manner in which the traditional manufacturing and service-oriented industries are functioning today. The first industrial revolution was triggered with the invention of steam engine and also led to mechanical production. The invention of electricity and assembly lines resulted in the second industrial revolution where mass production became feasible. The third industrial revolution was driven by computer, digital technology and the internet. The future technologies have resulted in the fourth industrial revolution. The new age technological innovations and inventions such as the automated robots; big data and analytics; augmented reality; the cloud; cyber security; additive manufacturing; horizontal and vertical integration; the internet of things are transforming industrial production and labour relations. There is a drastic improvement in the entire chain of production ranging from design up to productivity, the speed and the quality at which the goods are produced. As a result of the new age technologies various concerns are raised especially its impact on the employment. Many labourers are rendered unemployed and redundant due to automation. The question that arises is whether we are approaching a jobless future?? The job market in India is also undergoing a transformation and posing many social, economic, legal and ethical challenges. Job structure is changing and the workers need to equip themselves with new skills to fit into the new jobs that are emerging as a result of technological innovation. The education system in any country plays a pivotal role in the overall development of an economy as it caters to the needs of the trained and skilled manpower. It is vital for the education system in the country to re-orient itself to cater to the needs of the students to fit into the changing paradigm. The focus of the education needs to be on imparting life-skills and to improve the thinking, problem-solving and decision-making ability of the individuals in a society. In the light of the above, it is also important to address and discuss the various changes, issues and challenges that are taking place in the labour market including the impact of these technologies on the working hours, wages, the working environment and the labour relations amongst others. 2019, Department of Law, University of North Bengal. All rights reserved. -
Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects
In this paper, a new nature-inspire meta-heuristic algorithm called future search algorithm (FSA) is proposed for the first time to solve the simultaneous optimal allocation of distribution generation (DG) and electric vehicle (EV) fleets considering techno-environmental aspects in the operation and control of radial distribution networks (RDN). By imitating the human behavior in getting fruitful life, the FSA starts arbitrary search, discovers neighborhood best people in different nations and looks at worldwide best individuals to arrive at an ideal solution. A techno-environmental multi-objective function is formulated using real power loss, voltage stability index. The active and reactive power compensation limits and different operational constraints of RDN are considered while minimizing the proposed objective function. Post optimization, the impact of DGs on conventional energy sources is analyzed by evaluating their greenhouse gas emission. The effectiveness of the proposed methodology is presented using different case studies on Indian practical 106-bus agriculture feeder for DGs and 36-bus rural residential feeder for simultaneous allocation of DGs and EV fleets. Also, the superiority of FSA in terms of global optima, convergence characteristics is compared with various other recent heuristic algorithms. 2021, The Author(s). -
Future research directions for effective e-learning
In recent years, with the rapid advancement of technology and the global shift towards digital education, e-learning has gained significant momentum from education sectors. However, there are still several challenges and areas for improvement in the field of e-learning. This work discusses several future research directions that contribute to the effective implementation and enhancement of e-learning in solving real world problems. Also, various components like pedagogical strategies, technology integration, learner support and engagement, assessment and evaluation, accessibility and inclusivity, professional development for educators, quality assurance and accreditation, and ethical and legal issues are explained towards implementation of e-learning. Hence, this chapter explains the effectiveness, accessibility, and inclusivity of e-learning as providing effective educational opportunities for learners globally. 2024, IGI Global. All rights reserved. -
Future perspectives on new innovative technologies comparison against hybrid renewable energy systems
The increase in the dispatchable amount of renewable energy and rural access to the point is proposed. The fuel is used to generate power and electrical energy for the machine. This causes the electricity to manage the single connection point to analyze the hybrid generations. Improving this hybrid generator of renewable power resources can be enabled for the analysis. Photovoltaic power sources have been introduced for converting the power loads and the dumps. The vehicle energy power management technique and the renewable energy system have been used for the analysis. This study shows how vehicle and renewable energy management can help develop geothermal against hydrothermal vents. Hydropower and vehicles can enable bioethanol for vehicle biodiesel. This study allows for the analysis of hydrothermal and biodiesel. In this study, the power of the energy enables the hybrid system, and the combination of the power generator to access the vehicle is proposed. 2023 -
Future Perspectives of Microplastic towards Environmental Assessment
Microplastic (MP) pollution is an outcome of the widespread use of non-biodegradable plastic and improper disposal. This leads to contamination of environmental resources, such as landfills, and all kinds of water reservoirs including but not limited to sea, fresh water, drinking water, and even wastewater. Recent reports have highlighted the presence of MPs in the human body, including blood, lungs, placentas, and breast milk, indicating the severity of the issue. It is thus crucial to eliminate these hazardous contaminants from the environment. One of the effective methods to address the concern while reducing the adverse effects is to remove the MPs at their discharge points. Nanomaterials with exceptional properties like high surface area, ease of functionalization, and high affinity toward various pollutants act as excellent adsorbents. In this chapter, we present an overview of emerging nanomaterial-based adsorbents, such as photocatalysts, metal-organic frameworks, carbon-based nanomaterials, and nanocomposites, for effective removal of MPs from aqueous media via adsorption, photo-catalysis, and membrane filtration. However, considering that the research in the area of MP pollution is still in its infant stage, we aim to provide a brief account of the strengths, weaknesses, and future research dimensions of nanomaterial-based adsorbents for removing MPs from aqueous media. 2025 selection and editorial matter, Nirmala Kumari Jangid and Rekha Sharma; individual chapters, the contributors. -
Future of knowledge management in investment banking: Role of personal intelligent assistants
Purpose: The studys objective focuses on investigating the involvement of Personal Intelligent Assistants (PIAs) in the Knowledge Management Process (KMP) in Investment Banking Companies leading to Industrial Revolution 5.0 leading to effective Organizational Knowledge Management. Design/Methodology: A Self-administered Survey Questionnaire was circulated to 695 employees of Investment Banking Companies operating in Bangalore, Mumbai, Delhi, Hyderabad, Chennai, and Pune using the Cluster Sampling method. The Covariance-based Structural Equation Modelling (CB-SEM) and Gradient Boosting Regression technique of Machine Learning were used to validate the hypothesis through JASP V.18 Software. Knowledge Creation, Knowledge Sharing, Knowledge Retrieval, Knowledge Application, and Organizational Knowledge Management are the crucial constructs considered in the study. Findings: The results revealed that Knowledge Application is the most influencing factor in effective organizational Knowledge management among the Investment Banks followed by Knowledge Sharing. It also emphasizes that they have a weak Knowledge retrieval process and minimal efforts taken to create knowledge within these banks. Implications: The PIAs can facilitate effective Data Analysis and research in managing vast data eliminating the repeated tasks in portfolio reconciliation and offering personalized recommendations to manage portfolios. It enables in compliance, risk management, client relationship management, real-time monitoring and leveraged decision-making through predictive analysis. The Author(s) 2024. -
Future of customer engagement through marketing intelligence
In the competitive world of contemporary business, the challenge of developing marketing strategies that bridge the gap between traditional and innovative techniques has become more critical than ever. As marketing shifts between physical and digital realms, companies grapple with the central question of how to navigate this evolution successfully. The key lies in data - the linchpin that can unravel vital problems in modern marketing. The need for sustainable and effective marketing strategies permeates all sectors, emphasizing the urgency for businesses to combine traditional methods with innovative approaches, such as harnessing alternative data and leveraging AI-based solutions. Future of Customer Engagement Through Marketing Intelligence emerges as a compelling solution to the pressing challenges faced by businesses in this transformative landscape. It offers a step-by-step roadmap, guiding readers on how market intelligence can utilize data and transform it into actionable insights. By emphasizing the crucial role of data in crafting great marketing strategies, the book advocates for a deep understanding of market-supported content and factual data. It asserts that marketing intelligence, encompassing data collection, analysis, and strategic utilization, is the key to becoming customer-centric, understanding market demands, and gaining a competitive advantage. Designed with a comprehensive and practical approach, the book's objectives align with addressing the emerging trends and challenges in customer engagement driven by marketing intelligence. It caters to a diverse audience, including marketing professionals, data analysts, business leaders, academics, researchers, consultants, technology developers, and policymakers. By delving into various topics, from AI-driven customer experiences to the application of advanced technologies like text mining and blockchain, the book serves as a valuable resource for navigating the evolving landscape of customer engagement and marketing intelligence. Ultimately, it stands as a beacon, illuminating the path toward sustainable and responsible customer engagement strategies in the ever-evolving world of marketing. 2024 by IGI Global. All rights reserved. -
Future Innovation in Healthcare by Spatial Computing using ProjectDR
Spatial Computation is the next step in the continuing convergence between the digital and physical realms. It is a set of inventions and developments that can better our lives through learning the real world, acknowledging and connecting our connection to, and traveling through various locations in the world. The lack of modern, precise, and effective diagnosis limits the rehabilitation of patients, despite technical advancements in medicines. The capabilities of spatial computing are expanded in a healthcare framework during the care and treatment of the patient. In this article, our purpose is to clarify the function of ProjectDR in the field of healthcare, which enables the display of medical images, such as CT scans and MRI results, directly on the patient's body in a manner that moves as patients do. 2021 IEEE. -
Future Inclusive Education
The United Nations (UN) Sustainable Development Goals (SDGs) ensure inclusive and equitable quality education for promoting lifelong learning. Inclusive education fosters an environment for access to quality education by addressing diversity and barriers that can cause exclusion. COVID-19 has reimagined Higher Education with new challenges and opportunities for the present and future. Digital divide, gender inequality, addressing specially-abled students, and a non-inclusive learning environment are the major barriers to inclusive education. Inclusive education ensures that no one leaves behind, and higher education institutes can enhance their capacity building to promote inclusivity for the common good. Employability is one of the key concepts in higher education that builds the workforce and contributes to nation-building. With COVID-19, nature of work has seen radical changes; hence, graduate attributes have evolved with the 21st-century skills. The chapter emphasizes the role of inclusive education and reimagining higher education with suggestions to using existing strategies in life-long and futuristic inclusive learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
Fusion of medical image using STSVD
The process of uniting medical images which are taken from different types of images to make them as one image is a Medical Image Fusion. This is performed to increase the image information content and also to reduce the randomness and redundancy which is used for clinical applicability. In this paper a new method called Shearlet Transform (ST) is applied on image by using the Singular Value Decomposition (SVD) to improve the information content of the images. Here two different images Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are taken for fusing. Initially the ST is applied on the two input images, then for low frequency coefficients the SVD method is applied for fusing purpose and for high frequency coefficients different method is applied. Then fuse the low and high frequency coefficients. Then the Inverse Shearlet Transform (IST) is applied to rebuild the fused image. To carry out the experiments three benchmark images are used and are compared with the progressive techniques. The results show that the proposed method exceeds many progressive techniques. Springer Nature Singapore Pte Ltd. 2017. -
Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction
Stock market prediction is one of the most important financial subjects that have drawn researchers attention for many years. Several factors affecting the stock market make stock market forecasting highly complicated and a difficult task. The successful prediction of a stock market may promise attractive benefits. Various data mining methods such as artificial neural network (ANN), fuzzy system (FS), and adaptive neuro-fuzzy inference system (ANFIS) etc are being widely used for predicting stock prices. The goal of this paper is to find out an efficient soft computing technique for stock prediction. In this paper, time series prediction model of closing price via fusion of wavelet-adaptive network-based fuzzy inference system (WANFIS) is formulated, which is capable of predicting stock market. The data used in this study were collected from the internet sources. The fusion forecasting model uses the discrete wavelet transform (DWT) to decompose the financial time series data. The obtained approximation and detailed coefficients after decomposition of the original time series data are used as input variables of ANFIS to forecast the closing stock prices. The proposed model is applied on four different companies previous data such as opening price, lowest price, highest price and total volume share traded. The day end closing price of stock is the outcome of WANFIS model. Numerical illustration is provided to demonstrate the efficiency of the proposed model and is compared with the existing techniques namely ANN and hybrid of ANN and wavelet to prove its effectiveness. The experimental results reveal that the proposed fusion model achieves better forecasting accuracy than either of the models used separately. From the results, it is suggested that the fusion model WANFIS provides a promising alternative for stock market prediction and can be a useful tool for practitioners and economists dealing with the prediction of stock market. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Further studies on circulant completion of graphs
A circulant graph C(n, S) is a graph having its adjacency matrix as a circulant matrix. It can also be interpreted as a graph with vertices v0, v1,,vn?1 that are in one-to-one correspondence with the members of Zn and with edge set {vivj: i ? j ? S}, where S known as the connection set or symbol, is a subset of non-identity members of Zn that is closed under inverses. This work extends the study of circulant completion and general formulae for calculating circulant completion numbers in two different perspectives, one in terms of circulant span and the other in terms of the adjacency matrix. (2024), (SciELO-Scientific Electronic Library Online). All Rights Reserved.