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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. -
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. -
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. -
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. -
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. -
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. -
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. -
Hierarchical Demographic Classification Using Multi-output CNN
This work constructs a multi-output Convolutional Neural Network (CNN) for demographic classification, including age estimation, gender determination, race categorization, and geographical subcategories of South Asia, North Asia, Europe, and Africa. Sub-region classification is further carried out in the Indian subcontinent: Karnataka, Kerala, Tamil Nadu, Punjabi and Kashmiri. The proposed CNN is trained using a heterogeneous dataset and its performance is compared with some robust pre-trained models such as VGG16, ResNet50, InceptionV3, and DenseNet121. The proposed custom CNN architecture makes use of convolutional layers for feature extraction, dropout for regularization, and custom loss weights for balanced multi-task learning. Experimental results are showing high accuracy values of 97% for age estimation, 99.55% for gender classification, and 98.4% for region and sub-region classification. The performance of the model gives a sign of its ability to handle demographic diversity while striving to minimize bias compared to its currently existing solutions. This work provides a foundation for future advancements in hierarchical facial recognition and demographic classification with an emphasis on ethical AI and fairness in real-world applications, including overcoming dataset limitations, exploring hybrid models, and incorporating multimodal data to give more inclusivity and precision. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Lightweight Zero Trust Access Control with Behavior-Based Anomaly Detection in Cloud
As cloud services become more popular, static security models must give way to dynamic, identity-centric ones. This paper introduces a serverless AWS architecturebased Lightweight Zero Trust Access (LZTA) framework with Behavior-Based Anomaly Detection (BBAD) designed for the cloud. Through the use of AWS Lambda to process CloudTrail logs and DynamoDB to store profiles, our system automatically learns user behavior. Using this profile, a Lambda Authorizer at the API Gateway determines a risk score in real time for every access request, preventing unusual activity such as attempts from unidentified IP addresses. This scalable, reasonably priced frame- work proved to be an effective modern cloud security solution by successfully blocking simulated credential theft attacks with a latency of less than 150 ms while running at no cost within the AWS Free Tier. 2025 IEEE. -
An organocatalytic C-C bond cleavage approach: A metal-free and peroxide-free facile method for the synthesis of amide derivatives
A facile organocatalytic approach has been devised towards the synthesis of amide derivatives using 1,3-dicarbonyls as easily available acyl-sources under peroxide-free reaction conditions. This transformation was accomplished by the cleavage of the C-C bond in the presence of TEMPO as an organocatalyst and excludes the use of transition-metals and harsh reaction conditions. A broad range of substrates with diverse functional groups were well tolerated and delivered the products in high yields. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique. -
An exploration of attitudes toward dogs among college students in Bangalore, India
Conversations in the field of anthrozoology include treatment and distinction of food animals, animals as workers versus pests, and most recently, emerging pet trends including the practice of pet parenting. This paper explores attitudes toward pet dogs in the shared social space of urban India. The data include 375 pen-and-paper surveys from students at CHRIST (Deemed to be University) in Bangalore, India. Reflecting upon Serpells biaxial concept of dogs as a relationship of affect and utility, the paper considers the growing trend of pet dog keeping in urban spaces and the increased use of affiliative words to describe these relationships. The paper also explores potential sex differences in attitudes towards pet and stray dogs. Ultimately, these findings suggest that the presence of and affiliation with pet dogs, with reduced utility and increased affect, is symptomatic of cultural changes typical of societies encountering the second demographic transition. Despite this, sex differences as expected based upon evolutionary principles, remain present, with women more likely to emphasize health and welfare and men more likely to emphasize bravery and risk taking. 2019 by the authors. Licensee MDPI, Basel, Switzerland. -
The Difference is in the Details: Attachment and Cross-Species Parenting in the United States and India
The purpose of the current research was to explore changes in Indian attitudes and practices with pet dogs and cats and compare them with responses from the United States. Pet parenting, defined as the investment of money, emotion, and time in companion animals, is a form of alloparental care (care given by someone other than the offsprings biological parents). Pet parenting appears to emerge in cultures that (1) demonstrate high rates of urbanization, (2) have declining total fertility rates (average births per woman), and (3) support life orientations beyond reproduction (collectively called the second demographic transition). A total of 1,417 respondents (US, n = 991; India, n = 426) completed online surveys (one in each country) to compare demographic profiles, attachment (as measured by the Lexington Attachment to Pets Scale [LAPS]), and companion animal caretaking behaviors in each culture. Mann-Whitney U tests were used to compare Indian and United States populations on the LAPS and caretaking behaviors (titled CARES in our study). Our findings document the emergence of pet parenting in India with many similarities to the United States. However, cultural variations in how these societies engage with nonhuman animals result in nuanced differences. For example, when reporting terms used to refer to themselves (e.g., Mom/Dad, friend, owner) and their companion animals (e.g., kids, pet, animal), United States respondents were more likely to code switch to less familial terms when speaking to coworkers and strangers. Additionally, Indian respondents reported higher agreement with all three LAPS scales, and they also reported higher frequency of behaviors related to Affective Responsiveness and General Care. Both cultures reported a moderately high frequency of Training and Play, with the United States respondents reporting slightly more training than Indians. These differences suggest that philosophical disparities exist between the United States and India, shaping the practice of pet parenting. We suggest continued, cross-cultural investigation of changing norms surrounding companion animals and the emergence of pet parenting. 2021 International Society for Anthrozoology (ISAZ). -
Exploring the Influence of Ethnicity and Environmental Values on Eco-Entrepreneurship: A Structural Equation Modeling Approach
In today's world, sustainability is of immense importance due to population growth, pollution and resource depletion. Consequently, there is an urgent need to devise future-oriented strategies for sustaining life on Earth. The rise of green business and the Sustainable Development Goals (SDGs) reflect society's growing awareness and commitment to environmentally friendly living. Our research examines the link between eco-entrepreneurship and the SDGs among young adults who are the next generation of entrepreneurs. We aim to understand how these individuals plan to incorporate the SDGs into their future business. Conducted primarily through surveys of 17- to 26-year-olds, our research uses the Statistical Equation Model (SEM) to analyze the relationship between eco-entrepreneurship, the SDGs and today's youth. In addition, we examine how current educational practices influence young adults' attitudes toward sustainability. By delving into these aspects, our paper seeks to improve the understanding of how young adults, our future leaders, perceive and pursue green business and sustainable development goals, ultimately determining the importance of these concepts for our future. 2024 IEEE. -
Naksha: A Lightweight Visual Packet Capture Tool Based on Location-Aware Metric
Naksha is a Visual Packet Analyzer, and Sniffer, which provides multiple visual approaches for real-time packet analysis in a geographical domain which utilizes WebSockets and long-polling based protocols in LeafletJS. It inhibits a global map which is populated by Targets, visited by a home network, these targets are connected by a 'link' whose stroke-width is determined by the packet-length. Links are animated to be approaching at different speeds proportional to their propagation latency and congestion loads. They are also animated to subtly gain translucency as they approach their Time-to-live. These packet links also show the direction of the packet (i.e outgoing, or incoming packets) and they can be classified by colour to display categorical attributes like protocol. The WebSocket based server is loop-backed to the home network to provide for robust and rapid deployment. 2024 IEEE. -
Learning From Global Cultures for a Sustainable Tomorrow With the Help of Immersive Tech in Education
With rising global and domestic environmental and social challenges, integrating traditional cultural knowledge with immersive education technologies offers a path to sustainability. This chapter explores how Indigenous and local knowledge systems can merge with Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI) to foster deeper understanding and promote sustainable behaviors. It highlights traditional ecological practices in agriculture, water use, marine management, and architecture as models of sustainable living, emphasizing the ethical role of Indigenous education. The chapter also examines how immersive technologies enhance experiential learning, gamification, and cultural knowledge sharing, while aligning with the Sustainable Development Goals (SDGs). It envisions a future where educational systems use immersive tools to cultivate environmentally responsible, empathetic, and globally conscious citizens. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Design Cognition while using digital tools: A Distributed Cognition Approach
The use of digital tools in the conventional architecture design thinking process which derives its basis from sketching is followed in many colleges in India. Various shortcomings due to the integration of digital tools to the manual design process have been enumerated during the past 30 years. Digital tools provide affordances different from the manual sketching design process, the effects of which can be understood by adopting a distributed cognition approach. The paper builds on design cognition research while using externalization tools in the design process. It does so by developing a theoretical framework derived from distributed cognition and an understanding of visual thinking processes from design literature. The paper utilizes the distributed cognition framework by Zhang and Norman, to arrive at resultant affordances of externalization tools in design. The same is then utilized for a protocol study which was coded for its visual thinking components and other relevant codes. The same protocol study was also coded for ideation flow analysis. The findings pointed towards compromised visual thinking and reduced ideation while utilizing digital tools in quick conceptualization. 2021 ACM. -
Effect of Computer Learning on performance in early Architecture Education
A mixed cohort of students with different experience backgrounds join the architecture degree. While some are well familiar with the user interface of computer and 3-D digital tools, others are not. The effect of such prior knowledge and their corresponding digital and analog performance in a designed experiment was evaluated with a sample of 38 first-year students. This was done to understand the performance effects of previous computer learning in students. Computer learning of the sample was studied in terms of years of computer exposure, the number of software known, and knowledge of 3D software or SketchUp. The results suggest that none of the factors contributed to the digital performance of students. This provided suggestions regarding the computer teaching emphasis which should be placed on students having less computer learning. 2022, Rajarambapu Institute Of Technology. All rights reserved. -
An Alternative Deep Learning Approach for Early Diagnosis of Malaria
Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detection of Malaria using traditional investigation methods through blood samples and expert judgments was found to be time-consuming. In this paper, the authors introduced a Machine Learning automated system to eliminate the need for human intervention, which in turn enables early detection of malaria. The study has used various Deep Learning techniques such as traditional Convolutional Neural Network (CNN), VGG19, ConvNeXtXLarge, ConvNeXtBase, ConvNeXtSmall, ConvNeXtTiny, InceptionResnetv2, Xception, DenseNet169, EfficientNetB7, MobileNet, ResNet50, and NasNetLarge as base models. These models have been trained and tested with microscopic blood smear images dataset and observed that ConvNeXtXLarge detects malarial parasites with an accuracy of 96%. The proposed method outperforms the existing approaches in terms of both accuracy and speed. The findings of this work can contribute to the development of more accurate and efficient automated systems for early detection of Malaria. 2024 IEEE. -
Dynamic task distribution model for on-chip reconfigurable high speed computing system
Modern embedded systems are being modeled as Reconfigurable High Speed Computing System (RHSCS) where Reconfigurable Hardware, that is, Field Programmable Gate Array (FPGA), and softcore processors configured on FPGA act as computing elements. As system complexity increases, efficient task distribution methodologies are essential to obtain high performance. A dynamic task distribution methodology based on Minimum Laxity First (MLF) policy (DTD-MLF) distributes the tasks of an application dynamically onto RHSCS and utilizes available RHSCS resources effectively. The DTD-MLF methodology takes the advantage of runtime design parameters of an application represented as DAG and considers the attributes of tasks in DAG and computing resources to distribute the tasks of an application onto RHSCS. In this paper, we have described the DTD-MLF model and verified its effectiveness by distributing some of real life benchmark applications onto RHSCS configured on Virtex-5 FPGA device. Some benchmark applications are represented as DAG and are distributed to the resources of RHSCS based on DTD-MLF model. The performance of the MLF based dynamic task distribution methodology is compared with static task distribution methodology. The comparison shows that the dynamic task distribution model with MLF criteria outperforms the static task distribution techniques in terms of schedule length and effective utilization of available RHSCS resources. 2015 Mahendra Vucha and Arvind Rajawat. -
An ettective dynamic scheduler tor reconfigurable high speed computing system
High Speed Computing is a promising technology that meets ever increasing real-time computational demands through leveraging of flexibility and parallelism. This paper introduces a reconfigurable fabric named Reconfigurable High Speed Computing System (RHSCS) and offers high degree of flexibility and parallelism. RHSCS contains Field Programmable Gate Array (FPGA) as a Processing Element (PE). Thus, RHSCS made to share the FPGA resources among the tasks within single application. In this paper an efficient dynamic scheduler is proposed to get full advantage of hardware utilization and also to speed up the application execution. The addressed scheduler distributes the tasks of an application to the resources of RHSCS platform based on the cost function called Minimum Laxity First (MLF). Finally, comparative study has been made for designed scheduling technique with the existing techniques. The proposed platform RHSCS and scheduler with Minimum Laxity First (MLF) as cost function, enhances the speed of an application up to 80.30%. 2014 IEEE.