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Performance Analysis of Novel Compact Octagonal Shaped Fractal Antenna for Broadband Wireless Applications
Antenna plays an important role in any part of the communication system. It has to be designed very cautiously to provide improved system performance to meet the developments in wireless technologies with various design constraints such as small size, low cost, high data, low power consumption and wideband capabilities. Several efforts have been made by various investigators around the globe to amalgamate benefits of fractal structures with electromagnetic concepts and applications to reduce the size of the antenna without obstructing the performance of the antennas. This paper proposes a novel compact octagonal shaped broadband fractal antenna. The proposed antenna was designed on an inexpensive FR4-epoxy substrate and simulated using the High Frequency Structure Simulator. The antenna resonates in dual bands in 3.8 and 1GHz with lowest return loss of ?32.80dB and gain of 10.22dB while maintaining the VSWR in the 2:1 level. Attempts have been made to reduce the size and improve the bandwidth using fractal concept and truncation of ground plane. The fabricated antenna was verified experimentally and the results are agreeing with the simulations. The point of attraction of this antenna is the use of single patch for broadband coverage with easy fabrication. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Crown shaped broadband monopole fractal antenna for 4G wireless applications
This paper proposes a novel crown shaped fractal antenna design suitable for 4G wireless applications. One of the promising approaches in miniaturizing the antenna size is to use the fractal geometries. Several efforts have been made by various investigators around the globe to amalgamate benefits of fractal structures with electromagnetic concepts and applications. This paper outlines a new approach in designing broadband monopole 2.1 GHz fractal antenna. The design starts with square patch antenna and goes up to third iteration for obtaining better performance and impedance matching. The proposed antenna was designed and simulated using the HFSS EM simulator. Performance analysis of the antenna was done with characteristics such as return loss, VSWR, efficiency and radiation pattern found to be good at 2.1 GHz. Wireless application demands miniaturization in system as well as antenna size with better performance, hence attempts have been made to reduce the size and improve the gain, efficiency and bandwidth of the proposed antenna. 2017 IEEE. -
Identification of Phishing URLs Using Machine Learning Models
In this study, we provide a machine learning-based method for identifying phishing URLs. Sixteen features, including Have IP, Have At, URL Length, URL Depth, Non-standard double slash, HTTPS domain, Shortened URL, Hyphen Count, DNS Record, Domain age, Domain active, iFrame, Mouse Over, Right click, Web Forwards, and Label, were extracted from the 600,000 URLs we gathered as a dataset of legitimate and phishing URLs. We then used this dataset to train a variety of machine learning models. These included standalone models such Naive Bayes, Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). We also used ensemble models likeHard Voting, XGBoost, Random Forests, and AdaBoost. Finally, we used deep learning models such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN).On evaluation of performance metrics like accuracy, precision, recall, train time and prediction time it was found that XGBoost provides the best performance across all categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Crime Analysis and Forecasting using Twitter Data in the Indian Context
Since the late 1990s, social media has added more features and users. Due to the rise of social media, blogs and posts by common people are now a part of mainstream journalism. Twitter is a place where people can share their ideas about culture, society, the economy, and politics. India's large population and rising crime rate make it hard for law enforcement to find and stop illegal activities. This article shows the use of Twitter data to analyse, forecast, and visualise criminal activity using statistical and machine learning models and geospatial visualisation techniques. This helps law enforcement agencies make the best use of their limited resources and put them in the right places. The research aims to present a spatial and temporal picture of crime in India and is split into three parts: Classification, Visualisation, and Forecasting. Crime tweets are identified using a hashtag query argument in the tweepy python package's search_tweets function, followed by substring-keyword classification. The visualisation uses gmaps and bokeh python packages for geospatial and matplotlib for analytical applications. The forecasting portion compares AR, ARIMA, and LSTM to determine the best model for time series forecasting of crime tweet count. 2023 IEEE. -
Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine LearningAlgorithms
The concept of social media began to gain popularity in the late 1990s and has played a significant role in connecting people across the globe. The constant addition of features to old social media platforms and the creation of new ones have helped amass and retain an extensive user base. Users could now share their views and provide detailed accounts of events from worldwide to reach like-minded people. This led to the popularization of blogging and brought into focus the posts of the commoner. These posts began to be verified and included in mainstream news articles bringing about a revolution in journalism. This research aims to use a social media platform, Twitter, to classify, visualize, and forecast Indian crime tweet data and provide a spatio-temporal view of crime in the country using statistical and machine learning models. The Tweepy Python module's search function and '#crime' query have been used to scrape relevant tweets under geographical constraints, followed by substring-keyword classification using 318 unique crime keywords. The Bokeh and gmaps Python modules create analytical and geospatial visualizations, respectively. Time series forecasting of crime tweet count is performed by comparing the accuracy of Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressivee Integrated Moving Average (SARIMA) models to determine the best model. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Enhanced Automated Online Examination Portal Using Convolutional Neural Network
In recent years, the digital evolution of education has significantly shaped the landscape of learning, steering it away from traditional classroom settings towards more agile e-learning platforms. This shift has underscored the urgency for comprehensive online examination systems, tailored to meet the unique challenges and demands of virtual education. Online learning platforms have seen a rapid rise in popularity, given their flexibility, cost-effectiveness, and capability to cater to learners worldwide. Such a widespread audience brings along the challenge of conducting exams without the constraints of geography and scale. Traditional examinations, with their manual paper based formats, fail to fit within this digital mold due to their logistical challenges and inefficiencies. Consequently, an online examination system not only introduces convenience but also operational efficiency, eliminating many of the logistical nightmares associated with manual exams. While existing tools might provide online testing capabilities, the integration of Artificial Intelligent driven proctoring in this portal elevates the standards of academic integrity to unprecedented levels. The main aim of this article is to create online test platform with the support of Artificial Intelligence technology. The result detect the malpractice activity and electronic device usage detection while online examination. 2023 IEEE. -
Advanced hybrid SVPWM techniques for two level VSI
This paper brings an advanced class of hybrid SVPWM techniques for medium voltage drive applications with two-level inverter which employs multiple division of active vector time (MDAVT) switching sequences to reduce total harmonic distortion (THD) and switching loss. The proposed hybrid SVPWM techniques are categorised based on the principle of bus-clamping strategies. Multiple division active vector time (MDAVT) switching sequences are used in the proposed strategies. The newly developed MDAVT switching strategies produce PWM waveform for all odd and even pulse number and maintain the symmetry of the voltage waveform. This work compares different MDAVT switching sequences based on modulation index and location of the clamping position (zero vector changing angle) of a phase in a line cycle. The proposed techniques lead to the reduction in weighted total harmonic distortion of line voltage (Vwthd) as well as switching loss. The results point to the superior order of performance of the developed MDAVT sequences in the various ranges of operation of modulation index and power factor values. The superior harmonic performance and switching loss characteristics of the MDAVT PWM techniques over the conventional SVPWM is experimentally verifiedona415 V, 2 hp induction motor drive. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Design space exploration of optimized hybrid SVPWM techniques based on spatial region for three level VSI
The performance of a multilevel inverter depends upon design and selection of an appropriate modulation technique. Space vector pulse width modulation (SVPWM) technique offers more flexibility than other pulse width modulation (PWM) techniques. However, conventional SVPWM technique becomes more complex for multilevel inverter because of increased number of space vectors and redundant switching states. This paper presents a design space exploration method of hybrid SVPWM techniques for three level voltage source inverter (VSI) to reduce total harmonic distortion (THD) and switching loss over wide linear modulation range. A new parameter Harmonic Loss (product of weighted total harmonic distortion factor of the line voltage (Vwthd) and normalized switching loss) is introduced as an objective function, and a spatial region identification algorithm is proposed to determine the optimized switching sequences for hybrid SVPWM technique. Two optimized hybrid SVPWM techniques are proposed based on the optimized switching sequences for three level VSI. The proposed hybrid SVPWM techniques are implemented on a low cost PIC microcontroller (PIC 18F452) and applied on an experimental prototype of three phase three level VSI with an induction motor as load. The experimental results are demonstrated to validate the performance of the proposed hybrid SVPWM techniques. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. -
Artificial intelligence-internet of things integration for smart marketing: Challenges and opportunities
The convergence of AI and the internet of things (IoT) has revolutionized various industries, including marketing. This integration offers immense potential for enhancing marketing strategies through real-time data analysis, personalized customer experiences, and predictive analytics. However, it also presents several challenges that need to be addressed for successful implementation. This abstract explores the challenges and opportunities associated with integrating AI and IoT in smart marketing initiatives. It discusses the potential benefits such as improved targeting, increased efficiency, and enhanced customer engagement. Additionally, it examines the challenges such as data privacy concerns, interoperability issues, and the need for skilled personnel. Furthermore, the abstract delves into case studies and examples illustrating successful AI-IoT integration in marketing campaigns. It also highlights emerging trends and future directions in this domain, emphasizing the importance of addressing challenges to unlock the full potential of smart marketing. 2024, IGI Global. All rights reserved. -
Artificial intelligence: Blockchain integration for modern business
In the rapidly evolving landscape of modern business, the integration of artificial intelligence (AI) and blockchain technologies has emerged as a potent strategy to address various challenges and unlock new opportunities. This chapter presents a comprehensive overview of the integration of AI and blockchain, highlighting its significance and potential implications for businesses across diverse sectors. The synergy between AI and blockchain offers novel solutions for enhancing transparency, security, and efficiency in business operations. AI algorithms enable the automation of complex tasks, data analysis, and decisionmaking processes, while blockchain provides a decentralized, immutable ledger for secure and transparent data management. By combining these technologies, businesses can streamline processes, reduce costs, mitigate risks, and create new business models. Few key applications of AI-Blockchain integration in modern business include supply chain management, financial services, healthcare, identity verification, and intellectual property protection. 2024, IGI Global. All rights reserved. -
Emerging technology adoption and applications for modern society towards providing smart banking solutions
The rapid advancement of emerging technologies has brought significant transformations to various sectors, including banking and finance. This chapter explores the adoption and application of emerging technologies in modern society, particularly focusing on their role in providing smart banking solutions. Technologies such as artificial intelligence (AI), blockchain, internet of things (IoT), and biometrics are revolutionizing traditional banking practices, enabling enhanced security, efficiency, and personalized services for customers. Through a comprehensive analysis of current trends and case studies, this chapter highlights the impact of these technologies on improving customer experiences, streamlining operations, mitigating fraud risks, and fostering financial inclusion. Additionally, it discusses the challenges and opportunities associated with the integration of these technologies into banking systems, including regulatory concerns, data privacy issues, and the need for skill development among banking professionals. 2024, IGI Global. All rights reserved. -
SCN1A Genetic Alterations and Oxidative Stress in Idiopathic Generalized Epilepsy Patients: A Causative Analysis in Refractory Cases
Single Nucleotide Polymorphisms (SNPs) have found it be associated with drug resistance in epilepsy. The purpose of this study was to determine the role of SCN1A gene polymorphism in developing drug resistance in idiopathic generalized epilepsy (IGE) patients, along with increased oxidative stress. The study was conducted at a tertiary care hospital in Delhi, India. We recruited 100 patients diagnosed with IGE patients, grouped as drug-resistant and drug-responsive, and then further compared the SCN1A SNP rs10167228 A*/T analysis between the two groups. We utilized the PCR-RFLP technique to investigate the association between polymorphisms and refractory epilepsy. Serum HMGB1 levels were estimated using the ELISA technique to analyze oxidative stress in both groups. rs10167228 A*/T polymorphism genotypes AT and AA genotypes are significantly associated with an increased risk of developing drug resistance. Serum HMGB1, IL-1?, and IL-6 levels were significantly higher in drug-resistant cases, compared to the drug-responsive group. The association of SCN1A gene polymorphisms, in conjunction with raised oxidative stress, may be predictive of the development of drug-resistant epilepsy. The AT and AA genotypes of rs10167228 may pose a risk factor for developing drug-resistant epilepsy. 2023, The Author(s), under exclusive licence to Association of Clinical Biochemists of India. -
Opportunity Recognition, Career Decision-Making, Self-Efficacy and Social Entrepreneurial Intention among Higher Education Students
Building on the entrepreneurship cognition literature, the present research aims to develop a model to examine the direct and indirect effects of opportunity recognition, career decisionmaking and self-efficacy on social entrepreneurial intention. The research adopted a crosssectional design. The research was divided into three distinct studies, each conducted with a specific objective. The data collected for three studies included higher education students newlineacross India. Studies 1 and 2 aimed to develop and validate two scales, namely social entrepreneurial opportunity recognition and social entrepreneurial career decision-making following steps in tool construction. The sample size was 600 for study 1 and 845 for study 2. The social entrepreneurial opportunity recognition scale had 24-items that measures opportunity recognition with six motivating factors as the lower order constructs which are life experiences, social awareness, social inclination, community development, institutional voids, and natural option for a meaningful career. The social entrepreneurial career decision scale had 20 items focusing on the appraisal components in pre-entry social entrepreneurial career decision-making and has four factors, which are relevance, coping potential, knowledge and resources, and normative significance. Study 3 examined the direct and indirect effects of opportunity recognition, career decision-making and self-efficacy on social newlineentrepreneurial intention using a sample of 605 students. The findings show that opportunity recognition influences social entrepreneurial intention and is partially mediated by career decision-making. Furthermore, self-efficacy moderates the mediating role of career decisionmaking between opportunity recognition and intention. This research facilitates a profound understanding of social entrepreneurial cognition and pre-entry decision-making. -
Exploring the motivating factors for opportunity recognition among social entrepreneurs: aqualitative study
Purpose: This paper explores the motivating factors that lead to opportunity recognition among social entrepreneurs in India. Design/methodology/approach: The study followed an exploratory, qualitative design based on thematic analysis of the interview data collected from 13 Indian social entrepreneurs. Findings: The study identifies two aggregate factors that motivate social entrepreneurs: personal and contextual. Personal factors include life experiences, social awareness, social inclination since childhood, spiritual motives, the need for a meaningful career and entrepreneurial intention. Contextual factors included institutional voids, community development, the presence of a role model and volunteer experiences. Research limitations/implications: This study contributes to the social entrepreneurship literature by providing a model for motivating factors that lead to opportunity recognition. This study enables policymakers and social entrepreneurship educators to identify aspiring social entrepreneurs and provide target-specific support to them. Practical implications: This study enables policymakers and social entrepreneurship educators to identify aspiring social entrepreneurs and provide target-specific support to them. Originality/value: The study uniquely contributes to the social entrepreneurship field by offering deep qualitative insights into the motivational and opportunity recognition patterns of social entrepreneurship. 2024, Parvathy Viswanath and A. Sadananda Reddy. -
The Role of Cognitive Appraisal in Informed Decision-Making among Social Entrepreneurs: A Thematic Analysis
Social entrepreneurship (SE) is gaining momentum by providing innovative solutions to economic, social, and environmental problems by generating jobs and social inclusion. However, it involves different challenges that may lead to a negative appraisal. This study aimed to explore cognitive appraisal processes social entrepreneurs use to make informed decisions in their entrepreneurial journey. Interviews were conducted with 13 Indian social entrepreneurs, and the data were subjected to thematic analysis. The main themes were; appropriateness, implications, coping potential, and normative significance. The study proposes a cognitive model for the appraisal of SE. The study is important for aspiring social entrepreneurs to understand the evaluation components of appraisal to decide how appropriate SE is as a career for them. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Social entrepreneurial opportunity recognition among higher education students: scale development and validation
Purpose: This study aims to develop and validate a multidimensional scale to measure the motivating factors that lead to opportunity recognition in social entrepreneurship among higher education institute (HEI) students. Design/methodology/approach: The scale was developed through two phases; in phase 1, semi-structured interviews with social entrepreneurs and aspiring students were conducted to explore themes for item generation. Phase 2 included developing and validating the scale using exploratory (EFA) and confirmatory factor analysis (CFA). The sample included HEI students (n = 300 for EFA, n = 300 for CFA) with either academic background or volunteering experiences in social entrepreneurship. Findings: A 24-item scale is developed in the study, with six factors measuring the motivating factors influencing opportunity recognition in social entrepreneurship: life experiences, social awareness, social inclination, community development, institutional voids and natural option for a meaningful career. Research limitations/implications: The scale facilitates the development of theories and models in social entrepreneurship. The scale also enables policymakers and social entrepreneurship educators to understand the motivating factors that lead to opportunity recognition among students. It would help them to provide target-specific support to students. Originality/value: To the best of the authors knowledge, this study is the first attempt to develop a scale that measures opportunity recognition in social entrepreneurship based on specific motivating factors. The study used the model by Yitshaki and Kropp (2016) as the conceptual framework. This study is the first attempt to triangulate the models findings using a quantitative methodology and through the development of a measurement scale. Besides, the scale adds value to social entrepreneurship research, which lacks empirical research on HEI students. 2024, Emerald Publishing Limited. -
Are Narrow-line Seyfert 1 Galaxies Powered by Low-mass Black Holes?
Narrow-line Seyfert 1 galaxies (NLS1s) are believed to be powered by the accretion of matter onto low-mass black holes (BHs) in spiral host galaxies with BH masses M BH ? 106-108 M o. However, the broadband spectral energy distribution of the ?-ray-emitting NLS1s are found to be similar to flat-spectrum radio quasars. This challenges our current notion of NLS1s having low M BH. To resolve this tension of low M BH values in NLS1s, we fitted the observed optical spectrum of a sample of radio-loud NLS1s (RL-NLS1s), radio-quiet NLS1s (RQ-NLS1s), and radio-quiet broad-line Seyfert 1 galaxies (RQ-BLS1s) of ?500 each with the standard Shakura-Sunyaev accretion disk (AD) model. For RL-NLS1s we found a mean log() of 7.98 0.54. For RQ-NLS1s and RQ-BLS1s we found mean log() of 8.00 0.43 and 7.90 0.57, respectively. While the derived values of RQ-BLS1s are similar to their virial masses, for NLS1s the derived values are about an order of magnitude larger than their virial estimates. Our analysis thus indicates that NLS1s have M BH similar to RQ-BLS1s and their available virial M BH values are underestimated, influenced by their observed relatively small emission line widths. Considering Eddington ratio as an estimation of the accretion rate and using , we found the mean accretion rate of our RQ-NLS1s, RL-NLS1s, and RQ-BLS1s as , and , respectively. Our results therefore suggest that NLS1s have BH masses and accretion rates that are similar to BLS1s. 2019. The American Astronomical Society. All rights reserved. -
A Statistical Search for Star-Planet Interaction in the Ultraviolet Using GALEX
Most (?82%) of the over 4000 confirmed exoplanets known today orbit very close to their host stars, within 0.5 au. Planets at such small orbital distances can result in significant interactions with their host stars, which can induce increased activity levels in them. In this work, we have searched for statistical evidence for star-planet interactions in the ultraviolet (UV) using the largest sample of 1355 Galaxy Evolution Explorer (GALEX) detected host stars with confirmed exoplanets and making use of the improved host-star parameters from Gaia DR2. From our analysis, we do not find any significant correlation between the UV activity of the host stars and their planetary properties. We further compared the UV properties of planet host stars to that of chromospherically active stars from the RAdial Velocity Experiment (RAVE) survey. Our results indicate that the enhancement in chromospheric activity of host stars due to star-planet interactions may not be significant enough to reflect in their near- and far-UV broadband flux. 2020. The American Astronomical Society. All rights reserved.. -
Exploring the relationship between esg performance and profitability in Indian power companies
This study aims to assess the relationship and impact of Indian power companies' environment, social, governance (ESG) scores on firm performance. Since 2022, CRISIL Ratings Ltd publishes yearly ESG scores for 225 companies in India. The final 'ESG' score is a composite of individual scores for the three metrics: 'Environment', 'Social', and 'Governance'. This study intends to examine the influence of ESG scores on financial profitability measured by both net profit ratio and return on investment. The existence and degree of association between the variables is measured using correlation and simple linear regression analyses, respectively. The findings suggest that the overall ESG composite score is positively and significantly associated with firm profitability and moderately associated with returns. The 'Governance' metric stands out which has a strong and positive relationship with profitability in particular. These findings advocate that by investing in ESG initiatives, the chances of better financial performance are improved. 2024, IGI Global. All rights reserved. -
Kannada translation and validation of Wellman and Liu's theory of mind scale and children's social understanding scale in preschoolers
Background: Assessing theory of mind (ToM) in children is crucial for understanding social cognition. Wellman and Liu's ToM scale and the Children's Social Understanding Scale (CSUS) have been used to study ToM in children but are not available in the local language. Aim: This study aims to translate both scales into Kannada and validate them in preschool children. Methods: Following the rigorous WHO protocol, we meticulously translated and back-translated Wellman and Liu's ToM and CSUS into Kannada with the help of bilingual experts. Validation involved administering both scales to 118 preschool children aged 3 to 6 years from diverse urban and rural backgrounds in a cross-sectional study, ensuring the scales' applicability across different settings. Results: The Cronbach's alpha values for Wellman and Liu's ToM and the CSUS were 0.769 (95% CI 0.698 to 0.828) and 0.983 (95% CI 0.978 to 0.987), respectively, indicating high internal consistency. The test-retest reliability for Wellman and Liu's ToM scale domains ranged from 0.74 to 0.95, and for the CSUS, it was 0.99, demonstrating good reliability. Pearson's correlation between the domains of two scales ranged from 0.32 to 0.69, suggesting a moderate relationship. Conclusion: Our study findings demonstrate that Kannada translations of Wellman and Liu's ToM and CSUS have good internal consistency, test-retest reliability, and construct validity. These tools will be valuable for understanding social cognition in preschool children. 2024 Indian Journal of Psychiatry.