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Predicting and Analyzing Early Onset of Stroke Using Advanced Machine Learning Classification Technique
Around the world, stroke is the leading cause of death. When blood vessels in the brain rupture, they cause damage. Alternatively, blockage in a blood vessel that supplies oxygen and other nutrients may also lead to this disease. This study uses various machine learning models to predict whether someone will have a stroke or not. Different physiological features were taken into account by this study while using Logistic Regression; Decision Tree Classification; Random Forest Classification; K-Nearest Neighbors (KNN); Support Vector Machine (SVM); Nae Bayes classifier algorithm; and XGBoost classification algorithm - these were used for six different models to ensure accurate predictions are made. We will accomplish the finest exactness with Bayes cv look which may be a hyper-tuning classifier with 92.87%. This consideration can be utilized for future work by doing the increase and include designing on the dataset. It is constrained to literary information, so it might not continuously be right for foreseeing stroke. so utilize the datasets that contain pictures and work on those datasets. 2024 IEEE. -
Predicting a Rise in Employee Attrition Rates Through the Utilization of People Analytics
Modern organizations have a multitude of technological tools at their disposal to augment decision-making processes, with artificial intelligence (AI) standing out as a pivotal and extensively embraced technology. Its application spans various domains, including business strategies, organizational management, and human resources. There's a growing emphasis on the significance of talent capital within companies, and the rapid evolution of AI has significantly reshaped the business landscape. The integration of AI into HR functions has notably streamlined the analysis, prediction, and diagnosis of organizational issues, enabling more informed decision-making concerning employees. This study primarily aims to explore the factors influencing employee attrition. It seeks to pinpoint the key contributors to an employee's decision to quit an organization and develop a futuristic data driven model to forecast the possibility of an employee leaving the organization. The study involves training a model using an employee turnover dataset from IBM analytics, including a total of thirty-five features and approximately one thousand and five hundred samples. Post-training, the model's performance is assessed using classical metrics. The Gaussian Nae Bayes classifier emerged as the algorithm delivering the most accurate results for the specified dataset. It notably achieved the best recall (0.54) indicating its ability to correctly identify positive observations and maintained false negative of merely 4.5%. 2023 IEEE. -
Predictability and herding of bourse volatility: An econophysics analogue
Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation. Bikramaditya Ghosh, Krishna M.C., Shrikanth Rao, Emira Kozarevi?, Rahul Kumar Pandey, 2018. -
Precursor to employee engagement AMID knowledge workers
The main objective of this study is to critically analyze the precursor to employee engagement. The research methodology used in this research is descriptive research. In primary data, responses are collected through well framed questionnaires and direct interaction with the employees to selected sample of 550 respondents of information technology organisations in Bengaluru City. The questionnaire consists of 20 questions based on employee engagement precursor. To reduce the dimension of this an exploratory factor analysis was carried out and 3 factors explaining 65.26% of the variance were derived. The 3 precursors identified as professional contentment (Cronbach's alpha 0.940) career development (Cronbach's alpha 0.836) and job enrichment (Cronbach's alpha 0.826). The current study adds to the research pointing at precursors to employee's engagement among knowledge researcher. Medwell Journals, 2017. -
Precision Corn Price Prediction with Advanced ML Techniques
In the ever-evolving corn market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates and external factors to enhance forecasting accuracy in the corn market. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the corn market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in corn price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, Adaboost, and ARIMA, coupled with the interpretability of SHAP values, the study aims to improve prediction accuracy in the corn market. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into corn market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the corn sector. 2024 IEEE. -
Precise surface molecular engineering of 2D-Bi2S3 enables the ultrasensitive simultaneous detection of dopamine, epinephrine, serotonin and uric acid
Multiple biomolecule detection at a single read is an emerging and highly desirable technology in point-of-care diagnostics. Thus, functional nanoscale materials with high precision and stability at an affordable cost are required to fabricate adaptable multiplex biosensing devices with exceptional performance. Herein, an ultrasensitive molecularly engineered 2D-Bi2S3 biosensor is developed via a two-step synthetic approach. Simultaneous detection of dopamine (DA), epinephrine (EP), serotonin (ST), and uric acid (UA) is achieved at the nanomolar level. The surface molecular engineered 2D-Bi2S3 by 4-mercaptobenzoic acid (MBA) exhibits a well crystalline nature and consists of 36 stacked layers with creased-paper-like morphology after an MBA molecule has been precisely linked at the basal plane of Bi2S3. Bi2S3-MBA's surface/vibrational spectroscopic and scanning tunneling microscopic studies demonstrate the Bi2S3-MBA electronic nature and the linked molecule present on the Bi2S3 surface with a comparatively large random distribution of MBA molecules at the basal plane than the edge plane. The density functional theory (DFT) calculation verifies the proposed molecular interaction mechanism. The success of this unique surface molecular engineering strategy, which effectively modified the electronic and surface configuration of the 2D-Bi2S3, offers an exciting possibility for building different variants of the versatile biosensor for real-world diagnostic device applications. 2024 -
Pre-Service and In-Service Teachers Perceptions of Using Virtual Reality Tools in Teaching
This paper explores pre-service and in-service teachers perceptions of virtual reality (VR) technology as a teaching and learning tool in the classroom in India. The study aimed to answer four research questions, including the adoption rate of VR technology among teachers, their confidence levels in teaching using VR technologies compared to digital technologies, attitudes towards using VR technology, and the usefulness of different uses of VR technology. The survey conducted among 102 teachers found limited adoption of VR technology, lower confidence levels in using it, but willingness to use it in the future. The paper recommends providing adequate training and support to increase teachers confidence in using VR technology in their teaching practices. The study also suggests that strategies to promote VR technology should consider gender differences in attitudes towards it. Overall, the research concludes that teachers view VR technology as having potential benefits for learning and teaching across various uses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Pre Packaged Insolvency - Exploring An Alternative Framework For Bankruptcy Resolution In India
This article is a review of literatures on the need for alternative bankruptcy resolution framework in India. The study explores the context & background to the recent initiation of limited Pre-Packaged Insolvency in India. The article makes a strong case for having a private & pre-negotiated mode of debt resolution along with the existing CIRP framework in India. The article provides a comparative perspective of CIRP and Pre-pack driven resolution model in India. The research paper also addresses some of the potential challenges & concerns related to initiation of pre-pack in India & accordingly discusses the relevant safeguards for the same. Lastly, the study also provide a brief view of pre-pack model currently practised in USA. The Electrochemical Society -
Pre and Post Operative Brain Tumor Segmentation and Classification for Prolonged Survival
The aim of this research was to provide a detailed overview of the techniques in detecting and segmenting meningioma brain tumor in pre- and post-operative MRI images and classify for presence of meningioma thereby giving an early diagnosis to decrease the death rate. This study examines trending techniques for brain tumour segmentation and classification in Magnetic Resonance (MR) images of pre and post-surgery. For the segmentation and anomalies in the brain categorization, several approaches such as regular machine learning techniques (K-mean bunching, Fuzzy C mean grouping etc.), Deep Learning-based approaches (CNN, ResNET, Dense Net, VGG etc.), classical algorithms (Snake contour, watershed method etc.), and hybridization approaches were applied, according to the analysis. Information base, for example, BRATS, Fig-Share, EPISURG or TCIA can be taken to gather clinical pictures which principally contains of 2 classifications, pre and post pictures of Brain tumor. The multiple processes of brain tumour segmentation methodologies, such as preprocessing, feature extraction, segmentation, and classification, are also explained in this work. The task of segmenting residual and recurrent tumors differs greatly from that of segmenting tumors on baseline scans before surgery. This study shows that each approach has its own set of pros and limitations, as well as notable findings in terms of precision, sensitivity, and specificity, according to the comparison research. The use of segmentation approaches to determine success and reliability has been discovered. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Practices for measuring business in construction engineering organizations /
Patent Number: 202221034683, Applicant: Dr. Anil Zende.
The fundamental drives of every organization are profitability and achievement. The sustainability among these organizations relies on numerous elements that seem to have a substantial influence on performance. Estimating the implementation of sustainability organizations helps to discover weaknesses in terms of enhancing its productivity and profitability. Because of the enormous diversity of construction companies, it is harder for development organizations to develop or sustain a scientific approach for measuring their present effectiveness. Previous research utilized questionnaires and scientific and management consultations. -
Practical Benefits of Using AI for More Accurate Forecasting in Mental Health Care
Artificial Intelligence (AI) is the general term for being able to make computers do things that require human-like intelligence. AI is the novel idea of the computer pioneers like Alan Turning and John von Neumann in the 1940s. Their novel intuition towards making machines think is the key start for this AI technology evolution. As shown in Fig. 1, the first milestone of AI happened in the year 1956 when it was proved by a group of researchers that a machine could solve any problem with the use of an unlimited amount of memory. Here they named this program General Problem Solver (GPS). 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Powerlessness in the moral self: a social cognitive perspective on drug users
Powerlessness resides in devalued self-images of drug users. This study, drawing on social and moral psychology, examined the moral functioning of drug users compared to non-drug users. Self-reported data concerning moral identity and moral judgment on drug use were assessed and compared between groups. Drug users appeared to have significantly weaker moral identity centrality and pro-drug moral judgment than non-drug users. They also showed dissociation in the relationship between moral identity and moral judgment. As a result, the study proposed a moral identity model of drug use to better approach social cognitive powerlessness in drug users moral self. 2021 Taylor & Francis Group, LLC. -
Powering Ahead: Navigating Opportunities and Challenges in the Electric Vehicle Revolution
The technology is clearing ways for buzz in the market brimming with innovative items and new prospects. The government has planned to shift to electric vehicles by 2030, whether it is for personal or commercial use. As innovative improvements are developing quickly, it is blasting the market with the EVs industry which expected to transform the future (Rajkumar S, in Indian electric vehicle conundrum: a tale of opportunities amid uncertainties, 2020). Volvo company has also announced that it will be fully electric by 2030 (https://gadgets.ndtv.com, in Volvo to go all electric by 2030, sell exclusively online, 2021). It is expected that EVs will generate more demand for electricity and help in settling the focus on resources problem. It will also help in improving the financial feasibility of power sector projects. In India, there is more dependency on renewable energy so this is a chance to be independent and provide cheap power to the people. The EVs are more economical than petrol or diesel vehicles. The government is also giving incentives to the makers of electric vehicles. GST on electric vehicles is 12% as compared to petrol and diesel vehicles with 28% GST. As per the Electricity Act, 2003, a distribution license is needed to supply power from respective state electricity regulatory commissions. Another challenge is that charging the EVs will lead to a rise in the demand of electricity which is risky for the electricity distribution companies (www.livemint.com, in Indias electric vehicle drive: challenges and opportunities, 2017). Indians are very price conscious. A recent study revealed that Indians are ready to compromise on more charging time, but they are not ready to pay higher price for EVs (Gupta NS, in Electric vehicle adoption in India: study reveals three tipping points, 2020). From Fig.1, it can be seen that in 2014 investment in EVs was $2.2 billion which has increased to $406 billion in 2019 (Shanti S, in The road to green: what makes electric vehicle adoption a challenge for India. 2020). This shows that people are shifting toward EVs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Power quality improvement strategy for non-linear load in single phase system
Widespread use of non-linear loads in today's world scenario, increased the harmonic current injection into the grid. The harmonic current play a vital role in deteriorating the power quality of the grid. The non-linear loads may be either, single phase or a Three phase loads. In this paper, a control strategy for single phase shunt active filter is discussed, in mitigating the harmonics flowing into the grid. The extraction of reference signal of shunt active filter is designed, using instantaneous reactive power theory. Here load is considered as diode rectifier which is feeding a resistive inductive load. A complete control strategy and analysis is done in MATLAB/Simulink environment. 2016 IEEE. -
Power quality disturbance mitigation in grid connected photovoltaic distributed generation with plug-in hybrid electric vehicle
In the last twenty years, electric vehicles have gained significant popularity in domestic transportation. The introduction of fast charging technology forecasts increased the use of plug-in hybrid electric vehicle and electric vehicles (PHEVs). Reduced total harmonic distortion (THD) is essential for a distributed power generation system during the electric vehicle (EV) power penetration. This paper develops a combined controller for synchronizing photovoltaic (PV) to the grid and bidirectional power transfer between EVs and the grid. With grid synchronization of PV power generation, this paper uses two control loops. One controls EV battery charging and the other mitigates power quality disturbances. On the grid connected converter, a multicarrier space vector pulse width modulation approach (12-switch, three-phase inverter) is used to mitigate power quality disturbances. A Simulink model for the PV-EV-grid setup has been developed, for evaluating voltage and current THD percentages under linear and non-linear and PHEV load conditions and finding that the THD values are well within the IEEE 519 standards. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Power Line Communication Parameters in Smart Grid for Different Power Transmission Lines
In an electrical power system smart grid is a network that renewable energy sources along with smart devices. Communication capabilities of the conventional grid can be improved by the inclusion of superior sensing and computing abilities. Device control, remote management, information collection, intelligent power management is achievable by using communication networks. Wired communication technology is used because of its advantages like reliable connection, free from interference, and faster speed. In this paper, the data communication parameters have been analyzed using Power Line Communication (PLC) with various lengths of transmission lines. An orthogonal Frequency Modulation scheme is used to obtain the minimum BER.MATLAB Programming has been carried out and the results have been compared with the standards and found to be satisfactory. 2021 IEEE. -
Power law in tails of bourse volatility evidence from India
Inverse cubic law has been an established Econophysics law. However, it has been only carried out on the distribution tails of the log returns of different asset classes (stocks, commodities, etc.). Financial Reynolds number, an Econophysics proxy for bourse volatility has been tested here with Hill estimator to find similar outcome. The Tail exponent or ? ? 3, is found to be well outside the Levy regime (0 < ? < 2). This confirms that asymptotic decay pattern for the cumulative distribution in fat tails following inverse cubic law. Hence, volatility like stock returns also follow inverse cubic law, thus stay way outside the Levy regime. This piece of work finds the volatility proxy (econophysical) to be following asymptotic decay with tail exponent or ? ? 3, or, in simple terms, inverse cubic law. Risk (volatility proxy) and return (log returns) being two inseparable components of quantitative finance have been found to follow the similar law as well. Hence, inverse cubic law truly becomes universal in quantitative finance. Bikramaditya Ghosh, M. C. Krishna, 2019. -
Power Efficient e-Bike with Terrain Adaptive Intelligence
Electric bicycles or e-bikes are gaining momentum in the market as they are offering a smooth, noiseless and pollution free option for individual transportation in cities as well as in countryside. E-bikes are usually with a battery powered electric motor drive with an additional option for pedaling. In this work a low cost e-bike was designed and developed with a brushless DC hub motor with controllers. For smart control, smartphone was used a console and the e-bike can be controlled using a mobile application which was connected to the e-bike through Bluetooth. The controller will pick the gradient of the terrain and will control the power of the motor, which results in energy saving. Predicted range of the e-bike, speed, acceleration and total distance covered were displayed in the console along with the geographical position on the map and throttle control options. The bike with the proposed control tested and the results were giving a reduction in current drawn from the battery. 2019 IEEE. -
Power and Area Efficient Decimation Filter Architectures of Wireless Receivers
This paper reports on the synthesis and implementation of a digital decimation filter suitable for multi-standard transceivers. Decimation filter architectures used in transceivers must be capable of providing low power and less area. In this paper, three different architecture designs namely Decimation Filter with Conventional MAC Unit, Cascaded Multi-Standard decimation Chain and Hybrid structure are proposed to meet the demand of low power and area efficient digital decimation filter. The filter architectures are implemented using FPGA and its performances are tested. The architectures are tested using conventional number system and with two different encoding schemes of filter coefficients called canonic signed digit and minimum signed digit. The implementation results reflect that considerable reduction in area of 47.9% and power reduction of 28.6% are achieved using hybrid architecture, when compared with conventional MAC and cascaded chain architectures. 2016, The National Academy of Sciences, India. -
Pothole Detection and Powertrain Control for Vehicular Safety
A new era of automotive technology has begun with the rapid advancement of electric vehicles (EVs), which promise efficiency and sustainability. With electric vehicles (EVs) becoming an integrated part of the traction systems, there is a growing need for novel safety and performance-enhancing features. The development of an Adaptive Cruise Control (ACC) system for autonomous powertrain control and pothole detection in electric vehicles is examined in this paper. The paper focuses on integrating an intelligent system that can detect potholes and autonomously regulate the powertrain to improve both the driving experience and safety of electric vehicles. The system makes use of Jetson Nano as the processing unit for regulation of the EV powertrain. This board enables quick and accurate reactions to changing road conditions by facilitating real-time data analysis and decision-making. The powertrain regulation will be performed by controlling the acceleration and braking signal provided to the powertrain. 2024 IEEE.