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Feature extraction and diagnosis of dementia using magnetic resonance imaging
Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR - True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm. 2023, Bentham Books imprint. All rights reserved. -
Feature extraction and classification techniques of modi script character recognition
Machine simulation of human reading has caught the attention of computer science researchers since the introduction of digital computers. Character recognition is the process of recognizing either printed or handwritten text from document images and converting it into machine-readable form. Character recognition is successfully implemented for various foreign language scripts like English, Chinese and Latin. In the case of Indian language scripts, the character recognition process is comparatively difficult due to the complex nature of scripts. MODI script-an ancient Indian script, is the shorthand form for the Devanagari script in which Marathi was written. Though at present, the script is not used officially, it has historical importance. MODI character recognition is a very complex task due to its variations in the writing style of individuals, shape similarity of characters and the absence of word stopping symbol in documents. The advances in various machine learning techniques have greatly contributed to the success of various character recognition processes. The proposed work provides an overview of various feature extraction and classification techniques used in the recognition of MODI script till date and also provides evaluation and comparison of these techniques. 2019, Universiti Putra Malaysia Press. All rights reserved. -
Feature Based Fuzzy Framework for Sentimental Analysis of Web Data
Social mass media has emerged as a projectile platform for the evolution of web data. The sentimental Analysis where the huge textual online reviews are analyzed to extract the actual sentiment or emotions hidden in the reviews. In this paper an effective approach for sentimental analysis of web data is proposed which deploys the fuzzy based machine learning algorithm to accomplish fine-level sentiment analysis of huge online opinions by assimilating the fuzzy linguistic hedges influence on opinion descriptors. The seven layered categories are designed that uses SentiWordNet which has three stages: Pre-processing phase, Feature Selection Phase and Fuzzy based Sentiment Analysis phase. Various machine learning algorithms like AdaBoost, (IBK) K-Nearest Neighbour, (NB) Nae Bayes and (SVM)/SMO Support Vector Machine are used for classification. Jsoup is implemented for gathering web opinions which are subjected to initial processing task later applied with stemming and tagging. This fuzzy based methodology is investigated for Mobile, Laptops dataset, also compared with state-of-the-art approaches which demonstrate upper indication of 94.37% accurateness through Kappa indicators showcasing lesser error rates. The investigational outcomes are tested on training data using Ten-Fold cross validation which concludes that this approach can be efficaciously used in Sentimental analysis as an aid for online decision. 2019 IEEE. -
Fear of COVID-19, workplace phobia, workplace deviance and perceived organizational support: A moderated mediation model
This paper aims to test a moderated-mediation model examining therelationships between Fear of COVID-19, workplace phobia, work deviance behaviourand perceived organizational support among hotel employees. An online questionnaire was administered to collect data, to which 481 responded. Data was collected from full-time frontline employees working in the Maldivian hospitality industry. The moderated-mediation model explained 44% of the variance in workplace deviance behaviourscan be predicted bythe fear of COVID-19, perceived organisational support and workplace phobia. The findingsshowthat perceived organizational support reduces the negative impact of COVID-19 fear on workplace phobia and deviance. Results suggest that to reduce the negative effect of the pandemic, organisations should adopt support measures across different managerial levels at different scales rather than providing one-size-fits-all solutions. 2023 The Authors. Stress and Health published by John Wiley & Sons Ltd. -
Fear estimation-evidence from BRICS and UK
The paper aims to build a composite Fear Index for the BRICS countries and UK by adding new dimensions to the initial structure, such as overbought/oversold conditions and commodity impacts. The main purpose is to identify the degree in which fear really percolates down to all the market participants, respectively if this generates a certain asset transfer to Gold. The results point out the GMM model as the best fit for explaining the link between the Fear Index and the behaviour of market participants. It also confirms the transfer of assets to a safer asset class during the phases of high volatility on the market. Serials Publications Pvt. Ltd. -
Fear estimation evidence from BRICS and UK /
International Journal Of Applied Business And Economic Research, Vol.15(4), pp.195-207, ISSN: 0972-7302. -
FDI in Developing Nations: Unveiling Trends, Determinants and Best Practices for India
In the recent UNCTAD World Investment Report 2023, China has the highest FDI inflows among the developing countries, following Brazil, India, Mexico, and Indonesia. These five developing countries attracted more FDI inflows in the year 2022. However, among these five countries, China and the other four countries have a lot of differences in FDI inflows. So, this study investigates the factors helping China get more FDI inflows by analyzing the trends and determinants of FDI inflows. The study also compares all the selected countries to suggest the best practices India can adopt to enhance its FDI attractiveness. So, the study considered economic indicators like GDP, infrastructure, trade openness, and natural resources. Further, panel data analysis was used to investigate the determinants influencing FDI inflows, utilizing the Panel Autoregressive Distributed Lag (P-ARDL) model for the data from 1990 to 2022. The findings showed that trade openness, market size, and quality of infrastructure explain the attraction of FDI inflows in selected countries in the long run. Thus, it is important to implement policies that encourage international collaboration by raising trade, lowering corporate expenses, and making infrastructural investments. India's availability of a large consumer market, developed infrastructure, and government initiatives like 'Make in India,' and "Skill India"have pulled major FDI inflows. India should prioritize manufacturing, IT, and healthcare while improving infrastructure and streamlining regulations. 2024 IEEE. -
Faulty Node Detection Using Vertex Magic Total Labelling in Distributed System
Distributed system consists of huge number of nodes that are connected to a network, which is mainly intended and predominantly used for information sharing. Large users are prone to share data through the network and the stability and reliability of the nodes are remaining as the major concern in this system. Therefore, the inconsistent message transmission causes the nodes in the network to act differently, which would not be acceptable. A rapid method of malfunctioning nodes detection can improve the QoS of distributed computing environment. In this paper, a novel algorithm is proposed based on the calculation of vertex magic total labelling (VMTL) value for each and every node in the network. Upon receiving the message from the sender node, the receiver node will quickly detect the faulty node by comparing the VMTL pivot value (Pv). Experimental results show that the proposed approach leads to high true fault rate (TFR) detection accuracy compared to the false fault rate (FFR) detection. Finally, all the information related to the faulty nodes will be sent to the server node for further investigation and action. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
Fault diagnosis in a five-level multilevel inverter using an artificial neural network approach
Introduction. Cascaded H-bridge multilevel inverters (CHB-MLI) are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of CHB-MLI. This paper investigates the effects and performance of open and short switching faults of multilevel inverters. Output voltage characteristics of 5 level MLI are frequently determined from distinctive switch faults with modulation index value of 0.85 is used during simulation analysis. In the simulation experiment for the modulation index value of 0.85, one second open and short circuit faults are created for the place of faulty switch. Fault is identified automatically by means of artificial neural network (ANN) technique using sinusoidal pulse width modulation based on distorted total harmonic distortion (THD) and managed by its own. The novelty of the proposed work consists of a fast Fourier transform (FFT) and ANN to identify faulty switch. Purpose. The proposed architecture is to identify faulty switch during open and short failures, which has to be reduced THD and make the system in reliable operation. Methods. The proposed topology is to be design and evaluate using MATLAB/Simulink platform. Results. Using the FFT and ANN approaches, the normal and faulty conditions of the MLI are explored, and the faulty switch is detected based on voltage changing patterns in the output. Practical value. The proposed topology has been very supportive for implementing non-conventional energy sources based multilevel inverter, which is connected to large demand in grid. References 22, tables 2, figures 17. E. Parimalasundar, R. Senthil Kumar, V.S. Chandrika, K. Suresh. -
Fault analysis in the 5-level multilevel NCA DCAC converter
The existing neutral clamped active inverter has common mode voltage with the high frequency which can reduce the severity with less voltage gain. The traditional active neutral point clamped (APC) DCAC converter maintains great common mode voltage with high-frequency (CMV-HF) reduction capability so, it has limited voltage gain. The paper presents a new 5-level active neutral point clamped DCAC converter that can change voltage step-up in a single-stage inversion. In the suggested design, a common ground not only reduces the CMV-HF but also improves DC link voltage use. Compared with the traditional two-stage 5-level APC DCAC converter, the proposed design has lower voltage stresses and greater uniformity. While improving the overall efficiency, the suggested clamped DCAC converter saves three power switches and a capacitor. Modelling and actual tests have proven the suggested active neutral point clamped inverters overall operation, efficacy and achievability. The proposed circuit is finally tested with fault clearance capability. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Fault Analysis and Compensation in a Five Level Multilevel DC-AC Converter
Existing Neutral clamped active (NCA) inverters have the property of high frequency common mode voltage, which can reduce the severity with less voltage gain. A newly designed five level (5L) NCA inverter can capable have achieved voltage step-up with a one stage inversion process. The proposed circuit common ground enhances DC link voltage usage while also mitigating common mode voltage with high frequency. The proposed topology is more compact and has less voltage stress than the conventional two stage topology. The proposed circuit contains merely seven power switches and two capacitors, whereas the conventional topology has ten switches and three capacitors, resulting in a more efficient layout. The proposed topology is developed in the simulink platform, and the simulation results are validated in a proto-type model with a power rating of 2000 W to validate its feasibility and performance with fault clearance capabilities. 2023, TUBITAK. All rights reserved. -
Fault Analysis and Clearance in FL-APC DC-AC Converter; [Analyse et imination des dauts dans le convertisseur CC-CA FL-APC]
The traditional active neutral-point-clamped (APC) dc-ac converter maintains great common-mode voltage with high-frequency (CMV-HF) reduction capability, so it has limited voltage gain. This article presents a new five-level APC (FL-APC) dc-ac converter capable of voltage step-up in a single-stage inversion. In the suggested design, a common ground not only reduces the CMV-HF but also improves dc-link voltage usage. While comparing with traditional two-stage FL-APC dc-ac converter, the proposed design has lower voltage stresses and greater uniformity. While improving overall efficiency, the suggested clamped dc-ac converter saves three power switches and a capacitor. Modeling and actual tests have proven the suggested APC inverter's overall operation, efficacy, and achievability. The proposed circuit is finally tested with fault clearance capability. 2023 IEEE. -
Fatigue surface analysis of AL A356 alloy reinforced hematite metal matrix composites
This study intends to investigate how copper chill affects the fatigue behaviour of composites made of aluminium alloy A356 and hematite. It was cast by altering the weight fraction particles of hematite (0 to 12%wt in increments of 3%wt) by sand casting method with and without copper chills at its end to get isotropic and homogenous significant characteristics under liquid metallurgical way. The test specimens were prepared in accordance with ASTM specifications. Ducom-type fatigue testing equipment (rotating bending-low cycle fatigue) is used in experiments to examine fatigue behaviour. The micrographic images were taken with a scanning electron microscope (SEM) and interpreted uniform reinforcement of hematite particles, and X-ray diffraction (XRD) patterns were used to reveal microscopic details. The existence of the hematite particles and their phases was revealed by the X-ray diffraction analysis. The results show that the composites cast with copper chills have significantly greater fatigue strength than the casting obtained without copper chills. It was also observed that at 9%wt, copper chilled composite shows improve in fatigue strength about 10.2% as compared without chilled composites. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Fate of AI for Smart City Services in India: A Qualitative Study
With the rollout of the smart city initiative in India, this study explores potential risks and opportunities in adopting artificial intelligence (AI) for citizen services. The study deploys expert interview technique, and the data collected from various sources are analyzed using qualitative analysis. It was found that AI implementation needs a critical examination of various socio-technological factors to avoid any undesirable impacts on citizens. Fairness, accountability, transparency, and ethics (FATE) play an important role during the design and execution of AI-based systems. This study provides vital insights into AI implications to smart city managers, citizen groups, and policymakers while delivering promised smart city experience. The study has social implications in terms of ensuring that proper guidelines are developed for using AI technology for citizen services, thereby bridging the ever-critical trust gap between citizens and city administration. Copyright 2022, IGI Global. -
Fast Fashion Brands: Sustainable Marketing Practices and Consumer Purchase Behaviour; [Blagovne znamke hitre mode: trajnostne trne prakse in nakupovalno vedenje potronikov]
The fast fashion boom is faced with economic, environmental and social justice objections. Sustainable marketing initiatives have become a new style statement, and brands are shifting to environment-friendly manufacturing. This study explores how fashion apparel brands adopt sustainable marketing practices to promote sustainable purchase behaviour. A cross-sectional survey using a quantitative research design was followed to collect responses from fashion brand consumers. Variance-based partial least squares-structural equation modelling (PLS-SEM) was used to assess the hypothesized model. Two-step bootstrapping was conducted to explore the mediating role of brand perception in the relationship between sustainable marketing activity and brand loyalty. The study suggests that firms can support sustainable marketing practices by creating a brand image and building trust. This can influence consumers' perceptions of sustainability and promote brand loyalty. The study also emphasizes the significance of brand loyalty in developing sustainable purchase behaviour that endures over time. The study provides insights into sustainable marketing strategies and policies in indigenous markets. 2024, University of Ljubljana Press. All rights reserved. -
Fast and effective removal of textile dyes from the wastewater using reusable porous nano-carbons: a study on adsorptive parameters and isotherms
In the present study, recyclable porous nano-carbons (PNCs) were used to remove textile dyes (mainly methylene blue, methyl orange, and rhodamine B) from an aqueous environment. Due to their high surface area and mesoporous nature, PNCs exhibited extremely fast and efficient adsorption behavior. PNCs synthesized at an elevated temperature of 1000 C are used in batch experiments, as they showed maximum dye removal with high surface area. Batch mode was used to optimize operational parameters such as initial dye concentration, contact time, adsorbent dose and pH as a function of time. Within ~7 minutes of treatment, PNCs achieved a maximum removal efficacy of ~99 percent for methylene blue. The recyclability of PNCs was investigated, and it retained its efficiency even after seven cycles. The efficacy of PNCs in treating industrial water contaminated with methylene blue dye was assessed. Different adsorption isotherms were carried out to determine maximum amount of dye that can be adsorbed on to surface of PNCs. The maximum adsorption capacity attained using Langmuir isotherm for methylene blue was around 1216.54 mg g-1. Adsorption kinetics were applied on experimental data to identify the rate of adsorption. It was confirmed that novel onion peel-based porous PNCs were successful in removing methylene blue dye effectively with short duration in comparison with other dyes mainly rhodamine B and methyl orange. Graphical abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Farming Futures: Leveraging Machine Language for Potato Leaf Disease Forecasting and Yield Optimization
Crop yield prediction is of paramount importance in modern agriculture. It serves as a linchpin for ensuring food security, efficient resource management, risk mitigation, environmental sustainability, and socioeconomic development. Accurate predictions enable us to maintain a stable food supply, optimize resource allocation, and manage the uncertainties associated with climate and market fluctuations. By fostering sustainable farming practices, crop yield prediction also plays a crucial role in reducing environmental impact and promoting rural development. Integrating artificial intelligence (AI) and machine learning (ML) in modern agricultural practices offers the potential to revolutionize the way we produce food, making it more sustainable, efficient, and resilient. This study has demonstrated the effectiveness of convolutional neural networks (CNNs) in the classification of potato leaf disease, achieving remarkable results with a test loss of 0.0757 and a test accuracy of 0.9741. 2024 Taylor & Francis Group, LLC. -
Farmers' Protests, Death by Suicides, and Mental Health Systems in India: Critical Questions
Ongoing farmers' protests have once again brought back the spotlight on the agrarian crisis in India. Even though upstream factors that perpetuate farmers' suffering, including the role of the state in promoting agrocapitalism, have been discussed extensively by scholars and activists across the spectrum, mental health discourses almost always frame it as a mental health problem to be addressed by increasing access to psychopharmaceuticals. Drawing on developments around farmers' protests and analysis of articles published in flagship journals of largest professional bodies of clinical psychologists and psychiatrists in India, I highlight the intimate relationship between neoliberal state and farmers' distress to which the mental health system shuts its ears and eyes obscuring and downplaying socio-structural determinants of farmers' mental health. Copyright 2021 Springer Publishing Company, LLC. -
Farm food and beverage: An attractive element of gastronomy in agritourism
The admixture of agriculture and tourism creates new fields of area like agritourism. The primary activity of agritourism is providing unique agritourism attractions to the visitors. Among the interests, farm food and beverages act as substantial components that intrigue the visitors. Food gastronomy is connected with farm food and beverage and its inception. Tourists in the agritourist destination want to explore the culinary practices there. Hence, this book chapter provides an idea of the concepts of agritourism and gastronomy, the applications of gastronomy in agritourism, the significance and dimensions of farm food and beverage in agritourism, factors influencing travelers' food choices, the benefits of gastronomy in agritourism and the value-added advantage of gastronomy in agritourism. Food is always a determinant element of the quality of service in the tourist place. 2024, IGI Global. All rights reserved. -
Farm field security system using CNN and GSM module
Loss of crops and the destruction of livestock have been a major problem for many people in rural areas due to grass-fed animals whose food is derived from plants. According to research 32% are herbivores [1]. Reduced emissions from deforestation as well as deforestation are the main reason for wildlife moving towards urban areas. It results in wildlife infestation and human and animal conflicts. Therefore, compensating for the rapid loss of crops and the slaughter of livestock requires animal shelter and isolation in order to restrict the entry of animals into farm fields. The paper describes an effective and reliable way to identify and repel wildlife from farmland and to send real-time data to the farmer indicating animal attack on fields. An image of an animal will be obtained by convolution neural networks using intensive reading algorithms that provide a message to the farmer using the GSM module. It uses a user alert system and the animal scaring method. The test results show that the proposed algorithm has high visual accuracy. The detection level of the test set is achievable and the detection result is reliable. 2024 Author(s).