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Radar Cross Section (RCS) of HIS-based Microstrip Patch Array: Parametric Analysis
Low profile structures such as High Impedance Surfaces (HIS) are capable of modifying the scattering properties of a radiating structure. This paper presents the novel design of patch antenna/array with non-uniform HIS based ground plane. Two FSS elements of different dimensions are designed with different resonant frequencies. The performance of the high impedance surfaces has been carried out by varying the HIS dimensions and height of the substrate. Using the analyses, patch antenna/array with ground plane based on non-uniform configurations of HIS elements are designed. The radiation and scattering characteristics of microstrip patch antenna/array with HIS- based ground plane are compared to those with conventional PEC-based ground plane. A maximum of 8 dB RCS reduction has been achieved for patch array with non-uniform HIS layer. 2018 IEEE. -
How much can we trust high-resolution spectroscopic stellar chemical abundances?
To study stellar populations, it is common to combine chemical abundances from different spectroscopic surveys/studies where different setups were used. These inhomogeneities can lead us to inaccurate scientific conclusions. In this work, we studied one aspect of the problem: When deriving chemical abundances from high-resolution stellar spectra, what differences originate from the use of different radiative transfer codes? 2016 Proceedings of the 12th Scientific Meeting of the Spanish Astronomical Society - Highlights of Spanish Astrophysics IX, SEA 2016. All rights reserved. -
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE. -
Emprical Study of Crypto Currency and its Adoption Among Indians
This paper investigates many factors that impact cryptocurrency awareness and acceptance in the Indian market. Data were obtained from 376 volunteers of various ages across India. The following paper presented a framework based on EFA (Exploratory Factor Analysis), CFA (Confirmatory Factor Analysis), and SEM (Structural Equation Model). Technology awareness, recommendations to others, attitude, social influence, and openness to technical education were all responsible for bitcoin adoption. Meanwhile, trust and perceived risk were not accountable for the adoption of crypto currency. No significant factors directly responsible for the adoption or abandonment of crypto currencies were mentioned in the papers that were read. The Indian market is still not thoroughly studied regarding crypto currency and the population using it. It would create a massive opportunity for crypto currency to operate in the Indian market once the factors responsible for crypto currency adoption are known 2024 IEEE. -
Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning
Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. Later on, our investigation involved the application of machine learning models including nae Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (DT). Surprisingly, these models reached high test accuracies - ANN 93.2%, NB 90.4%, DT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients. 2024 IEEE. -
Automatic Measurement and Differentiation of Traffic Volume Count
Traffic volume in India is growing drastically over the past few decades. This leads to an increased need of constructing more highways and underpasses. In order to have the definite knowledge of traffic volume, and to design the width and thickness of the pavements, periodical conduction of traffic census is necessary. At present, the evaluation of traffic volume is conducted manually. This system is tiresome and lacks accuracy. The data obtained from the traffic census decides the sanction of new highways, underpasses, or flyovers which involves huge investments. Hence, the accuracy of this data is very critical. In this paper, we propose an automatic tool that helps to measure the traffic volume and differentiate the vehicles using video processing tools in MATLAB. The proposed algorithm consists of the following steps: i Foreground Detection ii Blob Detection iii Blob Analysis iv Vehicle differentiation Counting. 2018 IEEE. -
Nexus Between Credit Conditions, Financial Literacy, and Loan Accessibility Among Indian MSMEs
We examine the interplay among commercial bank loan terms, financial literacy, and formal loan accessibility for micro, small and medium enterprises (MSMEs). Despite recent strides in integrating MSMEs into commercial bank portfolios via micro-lending initiatives, persistent challenges hinder their access to formal credit. Drawing from empirical data and existing literature, this study explores the nuanced impacts of loan terms and financial literacy on SMEs ability to secure formal loans. Addressing gaps in prior research, we concurrently analyse borrower characteristics and credit regulations influence on formal loan accessibility. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Feature extraction of clothing texture patterns for classification
Different features are extracted for Pattern Recognition using an efficient algorithms like Scale Invariant Feature Transform, Rotation invariant Radon Transform and extracting statistical features of a texture image. Support vector machine with RBF kernel in Weka is used in this paper for classification. This paper shows method to classify the clothing texture patterns like strips, plaid, pattern less and irregular pattern. This paper also proposes a method which can be efficient method to apply for the real time natural texture patterns and colors recognition systems. This paper gives the experiments results and the proposed method to enhance the experiments accuracy in future scope. 2015 IEEE. -
Prediction of Depression in Young Adults Using Supervised Learning Algorithm
Over the years, mental health has achieved an essential role in the pertinent development of a human being, and a large part of the population is affected by it. The most commonly affected community being college-going students, and the most common disorders being Anxiety and Depression. Depression is a leading cause of suicide in individuals, where suicide is the second most prevailing reason for death among 1529-year-olds. This study aims to identify the different reasons and other factors associated with depression to predict and determine whether an individual faces depressive disorders. For this research purpose, the most appropriate classifier is selected. The absolute accuracy of the proposed model is 91.17%, i.e., the model can correctly predict whether an individual has depression 91.17% of the time. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Factors Affecting Data-Privacy Protection and Promotion of Safe Digital Usage
India is facing the problem of the digital divide. Being developing countries and with low literacy rates, digital knowledge among the public is weak. Those who know a bit about digital operations on smartphones and computers are not having complete knowledge of data security and its peculiarities. Therefore, this study aimed to find determinants of data-privacy anxiety among Indians and to understand their stress and anxiety during the use of digital applications in their daily routines, especially amid the COVID-19 scenario. The current study adopted an inductive qualitative exploratory approach to delve into the above issues. This study employed a reflexive thematic analysis method to analyse interview data of 10 participants across young-adult to middle-adult age groups of male and female gender. Participants belonged to middle socio-economic status having urban background. The study found 6 themes and 26 subordinate themes as determinants of data-privacy anxiety. Emerging themes from the data indicated at the systemic determinants of data-security anxiety, the paradox of learned helplessness and convenience preference among participants. This paper employed the Foucauldian lens of bio-power to discuss the circumscribing function of ill-structured knowledge dissemination approaches. This paper argues in favor of a critical pedagogy approach in educating people about digital security, dealing with data-privacy anxiety, and promoting safe digital usage among all generations of Indians. It also suggests measures of modifications in policies and documentation processes of major online platforms and apps to curb uncertainty and sense of insecurity among users. 2022 Copyright for this paper by its authors. -
Sales Prediction Scheme Using RFM based Clustering and Regressor Model for Ecommerce Company
Machine learning models are being used for better insights and decision making across many industries today. It shows to be quite useful for businesses in the ecommerce industry as well due to the vast amount of data generated and its potential. This research aimed to find insights on future sales of an ecommerce company [1]. The vast number of variables including both categorical and continuous variables under product data, customer information, transaction information, led us to implement a prediction model using regressors rather than just time series forecasting techniques. First an RFM (Recency, Frequency and Monetary) based clustering algorithm was used to get customer related information and then integrate those results into a regressor to achieve the desired goal of prediction of sales. Two schemes were tested one being predictions on individual clusters and the other where the clusters were one hot encoded back into the main data. Results show quite high accuracy of prediction. The high R-squared also indicated that our hypothesis of including the variables contributed significantly to the predicted sales values was correct in this case. This research fulfills an identified need to understand how machine learning algorithms can be implemented by multiple algorithms being integrated in sequential and logical orders thus helping derive business specific strategies rather than making it a mere technical process by providing empirical results about how the predicted sales values along with given inputs can contribute in business decision making relating to marketing, inventory management, dynamic pricing or many more such strategies. 2022 ACM. -
FEC & BCH: Study and implementation on VHDL
Channel encoding and Forward Error Correction is a crucial element of any communication system. This paper gives a brief overview of the fundamentals, mechanism and importance of Forward Error Correction. The design and implementation of a (63,36,5) BCH Codec is also projected in the later sections. All simulations are made on MATLAB R2018b and the VHDL implementations have been carried out using Xilinx Vivado 2018.2. 2019 IEEE -
Design and Verification of a Novel Anchor Shaped Double Negative Metamaterial Unit Cell
In this manuscript, a novel anchor-shaped double negative metamaterial is proposed. The structure is designed to resonate at 2.45 GHz. The unit cell is designed on a 1.6 mm thick FR4 substrate having a dielectric constant of 4.4, and simulated using Ansys HFSS. The unit cell exhibits a double negative behavior and negative refractive index behavior. The robust and popularly used Nicolson-Ross-Weir and Transmission-Reflection methods were implemented on MATLAB to extract and validate the metamaterial characteristics. This novel metamaterial unit cell covers 1 GHz to 4.8 GHz which is one of the most extensively researched and employed bands of the electromagnetic spectrum. The bandwidth performance of this new structure for double negative behavior is compared to other unit cells. It shows better performance with comparable size and outperforms the other geometries. This metamaterial is well-suited for a wide range of applications like wireless communication, biomedical applications in ISM (2.4 GHz) band and 5G communication services in the sub-6 GHz range. 2022 IEEE. -
Algorithmic Trading: Financial Markets Using Artificial Intelligence
This research study gives an in - depth view of the recent developments in the fields of Machine Learning (ML) and Reinforced Learning (RL) techniques as they are related to various models for forecasting and systems for financial trading. The practical usage of deep learning models, that incorporates Neural Networks such as Recurrent, Convolutional along with hybrid models integrating genetic algorithms with LSTM networks, for forecasting the stock market patterns as well as bank failures, and fluctuations in exchange rate which is addressed in this study in an in - depth review analysis of the latest literature. In addition to this it also investigates how trading algorithm performance as well as risk management can be enhanced by applying techniques of deep reinforcement learning. This study also demonstrates the enhanced, efficacy, precision and the profitability achieved by using these artificial intelligence methods as compared with conventional economic modelling and detailed technical study models by analysing a number of stock markets and different kinds of assets. 2024 IEEE. -
Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management
In the contemporary landscape of Human Resource Management (HRM), organizations are increasingly turning to advanced technologies to streamline employee management processes. This study explores the integration of machine learning (ML) techniques as a transformative solution for optimizing HRM practices, with a specific focus on employee management. By leveraging the power of ML algorithms, this research aims to enhance decision-making, efficiency, and overall effectiveness in HRM. The study encompasses a comprehensive analysis of existing HRM challenges, such as talent acquisition, performance evaluation, and employee retention, and proposes ML-based solutions to address these issues. By applying natural language processing, pattern identification, and predictive analytics, businesses may learn a great deal about employee behavior, performance patterns, and possible areas for development. HR professionals are more equipped to make well-informed choices, customize employee experiences, and put proactive talent development initiatives into action thanks to this data-driven approach. Additionally, the study examines the moral issues and difficulties surrounding the use of ML in HRM, stressing the significance of openness, justice, and privacy. By understanding and mitigating these concerns, organizations can successfully harness the transformative potential of ML in employee management, fostering a more dynamic and adaptive HRM framework. The study's conclusions add to the growing body of knowledge on the relationship between technology and HRM and offer useful advice to businesses looking to use cutting-edge approaches to improve labor management procedures. 2024 IEEE. -
Empirical estimation of multilayer perceptron for stock market indexes
The return on investment of stock market index is used to estimate the effectiveness of an investment in different savings schemes. To calculate Return on Investment, profit of an investment is divided by the cost of investment. The purpose of the paper is to perform empirical evaluation of various multilayer perceptron neural networks that are used for obtaining high quality prediction for Return on Investment based on stock market indexes. Many researchers have already implemented different methods to forecast stock prices, but accuracy of the stock prices are a major concern. The multilayer perceptron feed forward neural network model is implemented and compared against multilayer perceptron back propagation neural network models on various stock market indexes. The estimated values are checked against the original values of next business day to measure the actual accuracy. The uniqueness of the research is to achieve maximum accuracy in the Indian stock market indexes. The comparative analysis is done with the help of data set NSEindia historical data for Indian share market. Based on the comparative analysis, the multilayer perceptron feed forward neural network performs better prediction with higher accuracy than multilayer perceptron back propagation. A number of variations have been found by this comparative experiment to analyze the future values of the stock prices. With the experimental comparison, the multilayer perceptron feed forward neural network is able to forecast quality decision on return on investment on stock indexes with average accuracy rate as 95 % which is higher than back propagation neural network. So the results obtained by the multilayer perceptron feed forward neural networks are more satisfactory when compared to multilayer perceptron back propagation neural network. Springer International Publishing Switzerland 2016. -
Artificial Intelligence based Semantic Text Similarity for RAP Lyrics
Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google's Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%. 2020 IEEE. -
Spatio - Temporal Analysis of Temperature in Indian States
Data, the oil of the century, is available in multiple formats for various applications. It is collected, stored, and distributed across different use cases in various forms. Researchers study, analyse and use data for numerous analyses and predictions. There is an increase in demand and consideration of spatiotemporal data analysis. Analysing and obtaining insights from the spatiotemporal data are carried out by various researchers. Many investigations have started investigating the strategies for spatial-transient examination and applying spatial-transient information investigation procedures to different areas. Analysing spatiotemporal data has been an advanced task; with the help of various Python libraries, Spatio Temporal dataset about the temperature of states of India is analysed to support the harsh climate near the region of tropic of cancer. Across the decade, there has been a cyclic trend in the temperature, which keeps toggling yet increases over time. It remains a question of worry and genuine concern to predict climatic conditions. Spatio-temporal analysis of temperature in Indian states involves analysing the spatial and temporal variations in temperature across different states in India. The study can use various statistical and geographic information systems (GIS) tools. Spatio-temporal analysis of temperature in Indian states can provide valuable insights into the changing climate patterns in different regions of the country, which can be helpful for policymakers, researchers, and other stakeholders to make informed decisions related to climate change mitigation and adaptation. 2023 American Institute of Physics Inc.. All rights reserved. -
An Enhanced Deep Learning Model for Duplicate Question Detection on Quora Question pairs using Siamese LSTM
The question answering platform Quora has millions of users which increases the probability of questions asked with similar intent. One question may be structured in two different ways by two users, and answering similar questions repeatedly impacts user experience. Manual filtration of such questions is a tedious task, so Quora attempts to detect and remove these duplicate questions by using the Random Forest Model, which is not completely effective. As Quora contains question answers in the form of text data, different Natural Language Processing techniques are used to transform the text data into numerical vectors. In this research, the log loss metric acts as the primary metric to evaluate different models. The primary contribution is that the Siamese network is used to process two questions parallelly and find vectors representation of each question. The vectors computed by this method enables similarity detection which is more effective than existing models. GloVe word embedding is used to understand the semantic similarity between two questions. The random classifier is built as the base model and logistic regression, linear SVM and XGBoost model are used to reduce the log loss. Finally, a Siamese LSTM is proposed which reduces the loss dramatically. 2022 IEEE. -
Design, development and characterization of polyelectrolyte multilayer membranes for potential filtration applications
The present work projects to modify the surface of commercially available microfiltration membranes using polyelectrolyte multilayer assembly. This polyelectrolytic modification changes the nature and properties of the membrane such as porosity, absorption capacity, charge etc. which can be used for potential environmental application such as herbicide removal from water. Chitosan (CHI) and polystyrene sulfonate (PSS) were incorporated into nanostructured polymeric films on polyamide membrane using layer-by-layer (LbL) technique. The developed polyelectrolyte multilayer assembly were characterized by using UV-vis spectroscopy, attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy, thermo gravimetric analysis (TGA) and atomic force microscopy (AFM). 2020 Elsevier Ltd. All rights reserved.