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Stock Market Trend Analysis on Indian Financial News Headlines with Natural Language Processing
Predicting the stock movement in the real-time scenario has been the most challenging and sophisticated in business. This business is affected by several factors from physical to psychological as well as rational to irrational. So far only few aspects have been taken into account while breaking down the conclusion. Implementing sentiment analysis, a subfield of Natural Language Processing (NLP), from the news, social media or financial document, investors decide whether they should invest for the company. The results have shown a significant and a feasible method for predicting the stock market trend with higher accuracy. The current research has mainly focus on finding the sentiment score from the news headlines and finding the hidden trend from it. Further the trading signals are generated based on the prevailing trend and trends are executed by the automated trading system. Using this algorithm, traders can reduce the manual intervention in the buy and sell decisions related to the stock market. 2021 IEEE. -
Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp
HR firms help drive economic growth by facilitating the acquisition and retention of top talent, fostering innovation and optimizing operational efficiency. The stock prices of these firms serve as a nuanced representation of their standing in the market. However, predicting stock prices proves to be a complex task due to the dynamic nature of the market. This paper delves into finding the most effective approach for forecasting stock prices within the HR sector, employing a diverse range of machine learning techniques. The investigation encompasses utilizing statistical methods like Simple Moving Average, RSI, Stochastic Indicators, and VIX India data alongside 'Machine learning approaches such as Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Gradient Boosting, and Neural Network.' To augment the analysis, a comprehensive study is conducted, integrating both top-performing and bottom-performing HRM firms (Info Edge Ltd and Quess Corporation) based on market capitalization. The outcomes derived from this study aim to lay the groundwork for future research endeavors in the realm of stock predictions specific to the HRM industry. 2024 IEEE. -
Stock price forecasting using ANN method
Ability to predict stock price direction accurately is essential for investors to maximize their wealth. Neural networks, as a highly effective data mining method, have been used in many different complex pattern recognition problems including stock market prediction. But the ongoing way of using neural networks for a dynamic and volatile behavior of stock markets has not resulted in more efficient and correct values. In this research paper, we propose methods to provide more accurately by hidden layer data processing and decision tree methods for stock market prediction for the case of volatile markets. We also compare and determine our proposed method against three layer feed forward neural network for the accuracy of market direction. From the analysis, we prove that with our way of application of neural networks, the accuracy of prediction is improved. Springer India 2016. -
Stock price prediction based on technical indicators with soft computing models
Stock market prediction is a very tough task in the finance world. Since stock prices are dynamic, noisy, non-scalable, non-linear, non-parametric and complicated. In recent years, soft computing techniques are used for developing stock prediction model. The main focus of this study is to develop and compare the efficiency of the three different soft computing techniques for predicting the intraday price of individual stocks. The proposed models are based on Time Delay Neural Network (TDNN), Radial Basis Function Neural Network (RBFNN) and Back Propagation Neural Network (BPNN). The predictive models are developed using technical indicators. Sixteen technical indicators were calculated from the historical price and used as inputs of the developed models. Historical prices from 01/01/2018 to 28/02/2018, where the time interval between samples is one minute, are utilized for developing models. The performance of the proposed models is evaluated by measuring some metrics. Also, this study compares the results with other existing models. The experimental result revealed that the BPNN outperforms TDNN, RBFNN as well as other existing models considered for comparison. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
Stock Price Prediction using Deep Learning and FLASK
The forecasting of stock prices is one of the most explored issues, and it attracts the attention of both academics and business professionals. It is quite difficult to make predictions about the stock market, and it takes extensive research into the patterns of data. With the expansion of the internet and indeed the growth of social media, online media and opinions frequently mirror investor sentiment. The volatility and non-linear structure of the financial stock markets makes accurate forecasting difficult. One of the sophisticated analysis techniques that is being used by academics in a variety of fields is the neural network. In this paper, we proposed deep learning techniques for google stock price prediction. A dataset from Kaggle was collected and applied deep learning techniques RNN, LSTM variants. We achieved better results with Bidirectional LSTM. We also created a web app for stock prediction using Christ University python FLASK. 2022 IEEE. -
Stock Price Prediction Using RNNs: A Comparative Analysis of RNN, LSTM, GRU, and BiRNN
Stock price prediction is a crucial area of financial market research, having significant implications for investors, traders, and analysts. However, given the dynamic and intricate nature of financial markets - which are impacted by a wide range of variables such as economic statistics, geopolitical developments, and market sentiment - accurately projecting stock prices is intrinsically difficult. Conventional techniques frequently fail to fully capture these dynamics, producing predictions that are not ideal. Recurrent Neural Networks (RNNs), one of the most recent developments in machine learning, provide potential methods to overcome these obstacles. Despite their potential, the effectiveness of different RNN architectures in stock price prediction remains an area of active research. This study compares four Recurrent Neural Network (RNN) architectures - Simple RNN, Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BiRNN) - for forecasting the Nifty 50 index values on the Indian National Stock Exchange (NSE) from the year 2000 to 2021. Using a comprehensive dataset spanning two decades, we assess each model's performance using the metrics Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The data shows that the BiRNN model regularly outperforms the other models in all criteria i.e., MAE, MAPE, and MSE, indicating higher predictive accuracy. This study adds to the existing research by offering useful insights into the usefulness of RNN models, especially that of the BiRNN model for predicting stock prices, specifically in the setting of the Nifty 50 index. Our findings emphasize BiRNN's potential as a stock price prediction model and open new options for future research in this area. 2024 IEEE. -
Strategic Integration of HR, Organizational Management, Big Data, IoT, and AI: A Comprehensive Framework for Future-Ready Enterprises
This exploration paper proposes a comprehensive frame aimed at fostering unborn-ready enterprises through the strategic integration of Human coffers(HR), Organizational Management, Big Data, the Internet of Things (IoT), and Artificial Intelligence(AI). By synthesizing these critical factors, the frame seeks to optimize organizational effectiveness, enhance decision-making processes, and acclimatize proactively to evolving request dynamics. Through a methodical review of being literature and empirical substantiation, the paper delineates the interconnectedness of these rudiments and elucidates their collaborative impact on organizational performance and dexterity. likewise, it explores perpetration strategies and implicit challenges associated with espousing such an intertwined approach. This paper not only contributes to the theoretical understanding of strategic operation but also provides practical perceptivity for directors and directors seeking to navigate the complications of the contemporary business geography and place their associations for sustained success in a decreasingly digitized and competitive terrain. 2024 IEEE. -
Strategic Power Factor Management for Elevated Lift and Hoist Performance
The paper outlines the design and simulation of active power factor correction for a 100 hp induction motor using MATLAB/Simulink. In this system, the induction motor functions as the primary load, operating with a low power factor. Different load scenarios are simulated to examine the motor's performance. The current drawn from the supply is verified under varying conditions, both with and without the implementation of a variable capacitance bank. The power system network comprises apparatus such as Induction Motors, Power Transformers, and Induction Furnaces, contributing to a low power factor. The resultant low power factor leads to elevated energy consumption. To mitigate this, power factor correction is imperative. Utilizing a variable capacitance proves instrumental in enhancing the power factor. The capacitor compensates for a portion of the reactive power, consequently reducing the total reactive power drawn from the source. This reduction in reactive power contributes to an overall decrease in power consumption. The research focus is on the effective correction of the power factor for a 100 hp induction motor through comprehensive design and simulation using MATLAB/Simulink, providing valuable insights into the impact of variable capacitance on current draw under diverse load conditions. 2024 IEEE. -
Streamlined Deployment and Monitoring of Cloud-Native Applications on AWS with Kubernetes Prometheus Grafana
As organizations increasingly move their applications to the cloud, it becomes essential to have an efficient and cost-effective method for deploying and managing those applications. Manual deployment can be time-consuming, error-prone, and expensive. Additionally, managing logs and monitoring resources for each deployment can lead to even greater costs. To address these challenges, we propose implementing an automation strategy for deployment in the cloud. With automation, the deployment process can be streamlined and standardized across different cloud providers, reducing the potential for errors and saving time and resources. Furthermore, a central log system can be implemented to manage logs from different deployments in one location. This provides a unified view of all logs and allows for easier troubleshooting and analysis. Automation can also be used to set up monitoring resources, such as alerts and dashboards, across different deployments. Overall, implementing an automation strategy for deployment in the cloud can help organizations save time and resources while improving their ability to manage and monitor their applications. A centralized log management system can further enhance these benefits by providing a unified view of logs from all deployments 2023 IEEE. -
STREE: A Secured Tree based Routing with Energy Efficiency in Wireless Sensor Network
The Wireless Sensor Network (WSN) applications are today not only limited to the research stage rather it has been adopted practically in many defense as well as general civilians applications. It has been witness that extensive research have been conducted towards energy efficient routing and communication protocols and it has been reached to an acceptable stages, but without having a secure communications wide acceptance of the application is not likely. Due to unique characteristics of WSN, the security schemes suggested for other wireless networks are not applicable to WSN. This paper introduces an novel tree based technique called as Secure Tree based Routing with Energy Efficiency or STREE using clustering approximation along with lightweight key broadcasting mechanism in hierarchical routing protocol. The outcome of the study was compared with standard SecLEACH to find that proposed system ensure better energy efficiency and security. 2015 IEEE. -
Strength and ductility behaviour of FRC beams strengthened with externally bonded GFRP laminates
The repair and rehabilitation of structural members are perhaps one of the most crucial problems in civil engineering applications. One of the advanced techniques of strengthening the reinforced concrete members is done by fiber-reinforced polymer composites. FRP is very effective to repair and strengthen the structural members that have become structurally weak over their life span. FRP repair system provides an economically viable alternative to traditional repair systems and materials. This experimental study focuses on the flexural strengthening of fiber reinforced concrete beams externally bonded with FRP laminates of different thicknesses. Six beams were cast for the study and tested under a four-point bending system. Out of which two beams were served as a control beam, one beam was considered as a reinforced concrete beam and the other was fiber reinforced concrete beam. The fibers used in this investigation were steel fiber. The beams were strengthened with GFRP of 3 mm and 5 mm of woven roving type. The study parameters of this investigation included yield load, ultimate load, deflection, yield load deflection, ultimate load deflection, deflection ductility, energy ductility, and the beam was found to be very effective in the load-carrying capacity, deflection, and ductility when compared to the control specimen. The fiber-reinforced concrete beam exhibit an increase in ultimate deflection by 79.3% when compared to the control specimen. GFRP strengthened beams showed an increase in ultimate deflection by 18.75% to 94.06%. GFRP strengthened fiber reinforced concrete beams showed an increase in ultimate deflection by 7.8 to 13.125%. GFRP strengthened beams showed an increase in ultimate deflection by 54.7% to 81.88%. GFRP strengthened fiber reinforced concrete beams showed an increase in ultimate load-carrying capacity by 36.9% to 48.7%. The ductility for the specimens increases by 1.27% to 1.34%, compared to the controlled specimen. 2020 Elsevier Ltd. All rights reserved. -
Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study
There is a strong need for having an operative security framework which can help in making IoT (Internet of Things) devices more secure and reliable which can further protect from adversarial intrusions. Federated Learning, due to its decentralized architecture, has emerged as one of the ideal choices by the research practitioners in order to protect sensitive data from wide IoT-based attacks like DoS (Denial of Service) attack, Device Tampering, Sensor-Data manipulation etc. This paper discusses the significance of federated learning in addressing security concerns with IoT (Internet of Things) devices and how those issues can be minimized with the use of Federated Learning has been deliberated with the help of comparative analysis. In order to perform this comparative analysis, we investigated the published work in FL based IoT application for the last five years i.e., 2018-2022. We have defined a few inclusion/exclusion criteria and based on that we selected the desired paper and provided a comprehensive solution to IoT based applications using FL approach. Federated learning offers an optimistic approach to intensify security in IoT environments by enabling collaborative model training while preserving information privacy. In this paper a framework named Federated AI Technology Enabler (FATE) has been envisaged which is one of the recommended frameworks in safeguarding security and privacy measures of IoT devices. 2024 IEEE. -
Stress Level Detection in Continuous Speech Using CNNs and a Hybrid Attention Layer
This paper mainly targets stress detection by analyzing the audio signals obtained from human beings. Deep learning is used to model the levels of stress pertaining to this whole paper followed by an analysis of the Mel spectrogram of the audio signals is done. A hybrid attention model helps us achieve the required result. The dataset that has been used for this article is the DAIC-WOZ dataset containing continuous speech files of conversations between a patient and a virtual assistant who is controlled by a human counselor from another room. The best results obtained were a 78.7% accuracy on the classification of the stress levels. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Stress Management among Employees in Information Technology Sector Using Deep Learning
Information technology is one of the areas in India that is developing the quickest India's information technology (IT) administrations industry has become more merciless. The information technology area has been managing additional difficult issues like specialized development, administration enhancement, and worldwide overhauling starting from the beginning of this long period. Along these lines, it is unimaginable for everybody to adjust to the moving difficulties they experience in the field of information technology, which causes stress. Stress is something that individuals battle with for most of their lives. Albeit the information technology (IT) industry is notable for its hazardous turn of events and development, it is likewise portrayed by high worker burnout and stress levels. This theoretical proposes an original strategy for overseeing stress in the IT business that utilizes deep learning methods. This study utilizes deep learning calculations to expect, distinguish, and decrease stress makes all together location the earnest issue of stress among IT experts. The principal objective is to make a shrewd framework that can help organizations proactively recognize stress-related issues in their labor force and proposition specific cures. 2024 IEEE. -
Stress strain characteristics of reinforced hollow concrete block masonry melded with mesh reinforcement
Plain Masonry similar to unreinforced concrete, is resilient in compression and weak in tension. Masonry gains strength with age similar to concrete. Inspite of these resemblances, there exist numerous differences between masonry and concrete. The major difference is the regular pattern of horizontal joints(known as bed joints) at specific intervals along the height of walls introduce due to the method of construction of masonry. These bed joints make masonry a direction dependent material possessing orthotropic properties, unlike concrete which is usually regarded as isotropic atleast in the elastic range. Mechanical properties such as compressive strength, tensile strength, flexural strength are a pre-requisite as part of the design of masonry walls. The present study deals with the experimental study to evaluate the mechanical properties of hollow concrete block masonry specimens for varying cement mortar proportions melded with mesh reinforcement at bed joints. Parameters such as compressive strength, modulus of elasticity, failure pattern have been studied and compared for reinforced and unreinforced hollow concrete block prisms. The study showed higher compressive strength and improved elastic modulus for specimens with higher grade of mortar Published under licence by IOP Publishing Ltd. -
Structural analysis of log periodic and monopole antennas considering cyclonic, interference effects
The Broadband High Frequency (HF) Transmit and Receive Antenna System are used as Surface Waveover the Horizon Radars (SWOTHR) for surveillance application. HF Transmit & Receive antenna systemconsists of transmit antenna and receive antenna array operating in HF band 2 to 30?MHz, which have tobe installed near sea shore. The antennas are of Monopole and Log periodic Dipole wire mesh antenna (LPDA). The height of Monopole and LPDA depends on wavelength ? of antenna. For HF band, the height range of receive is from 5 to 25m and transmit is from 10m to 100m. In this study, 10m high monopole for receive and 55m high 60m long Log periodic antenna for transmit are considered. Structural analysis and design of these antennas is critical due to installation at sea coasts. Based on the application, receive antennas are designed as array type consisting of 64 numbers monopoles as 32 doublet's and transmit antennas are 2 numbers of LPDA. If the same height structures installed side by side as an array, wind interference is caused by the obstruction caused by a structure in the path of wind. The antennas are installing on sea coast subjected to cyclonic storms. Dynamic effect of cyclonic and interference of wind is studied. Wind loads are calculated as per IS: 875 part 3:2015. Antennas are analyzed using FEM software STAAD Pro Advanced Connect Edition. Both antennas are analyzedfor self-weight, wind loads considering cyclonic and interference factors. Natural frequency of structure is determined using modal analysis to examine the problems of wind induced oscillations and dynamic effects of wind. 2023 Author(s). -
Structural and morphological characterization of hydrothermally synthesized N-Carbon Dot @ Fe3O4 composites for heavy metal ion detection
Heavy Metal-ion contamination is one of the most serious issues facing day-to-day life. To address this issue, sensing and removal of heavy metal ions in contaminated water become indispensable. Carbon Dots are hydrophilic in nature with magnificent electron acceptor and electron donator and hence it has been used as fluorescent probes for sensing applications. The present study deals with the synthesis of N-Carbon Dot (N-CD) @ Fe3O4 composite which was successfully fabricated via the hydrothermal method. The surface structure and morphology of the synthesized composite were characterized using X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). The elemental analysis of a sample was characterized using Energy Dispersive Spectroscopy (EDS). Further, the phase occurrence and the molecular vibration were analysed using XRD and Fourier Transform Infra-Red Spectroscopy (FTIR). Finally, the optical studies were measured using Ultravioletvisible Spectroscopy (UV Vis) and Photoluminescence Spectroscopy (PL). The prepared composite exhibited noticeable fluorescence properties and has promising potential for the detection and removal of toxic heavy metal ions in water. 2022 -
Structural characterization of graphene layers in various Indian coals by X-Ray Diffraction technique
The results of the structural investigation of three Indian coals showed that, the structural parameters like fa & Lc increased where as interlayer spacing d002 decreased with increase in carbon content, aromaticity and coal rank. These structural parameters change just opposite with increase in volatile matter content. Considering the 'turbostratic' structure for coals, the minimum separation between aromatic lamellae was found to vary between 3.34 to 3.61 A for these coals. As the aromaticity increased, the interlayer spacing decreased an indication of more graphitization of the sample. Volatile matter and carbon content had a strong influence on the aromaticity, interlayer spacing and stacking height on the sample. The average number of carbon atoms per aromatic lamellae and number of layers in the lamellae was found to be 16-21 and 7-8 for all the samples. Published under licence by IOP Publishing Ltd. -
Structural characterization of paraffin wax soot and carbon black by XRD
From past few decades, an exponential increase in the research related to carbon nanomaterials and their excellent applications has been witnessed. Realizing the need for new potential precursors and cost effective production methods, we have investigated two precursors-paraffin wax soot (CS) and carbon black (CB). Structural and morphological features of the samples are analyzed by various techniques such as X-ray diffraction, high resolution scanning electron microscopy and electron dispersive spectroscopy. The lateral size of the aromatic lamellae, stacking height, the average spacing of the (002) crystallographic planes (d002) and aromaticity are found to be 15.12 44.30 3.57 0.912 and 15.26 43.23 3.68 0.986 respectively for paraffin wax soot and carbon black. Very low ? and ? band intensity ratio shows a low amount of disorder in the samples. SEM micrographs of the samples reveal non-uniform carbon nanospheres of particle sizes 26-94 nm. Asian Journal of Chemistry 2013. -
Structural Health Monitoring Using Machine Learning Techniques
Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE.