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Predicting Player Engagement in Online Gaming: A Machine Learning Approach
The aim of this research is to make precise forecasts on player participation in online game using state-of-the-art machine learning algorithms. Player engagement plays a crucial element in determining the success of online games because it affects player retention, satisfaction and monetization. By understanding and predicting engagement levels, game developers and marketers can enhance the gaming experience and develop strategies to keep players invested. This research involves a comprehensive analysis of player behavior data from an online gaming platform. The dataset includes various demographic and behavioral features such as age, gender, location, game genre, playtime hours, in-game purchases, game difficulty, sessions per week, average session duration, player level, achievements unlocked, and engagement level. The data was preprocessed through handling missing values, normalizing numerical features, and encoding categorical variables. Exploratory Data Analysis (EDA) was conducted to understand the distribution and relationships between different features. Multiple machine learning models were evaluated to predict player engagement levels, including Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM). These models were then compared through the accuracy, precision, recall, and F1-score metrics. In the comparison, XGBoost emerged as the best model. Since it is the best-performing model, we can make the feature importance analysis to identify the best factors for predicting engagement in the next step. The XGBoost model achieved the highest accuracy of 91%, demonstrating superior precision, recall, and F1-scores across all engagement levels (High, Medium, Low). Ensemble methods like XGBoost, Gradient Boosting, and Random Forest outperformed the SVM model, highlighting their effectiveness in handling complex datasets. 2024 IEEE. -
IoT-Based Smart Indoor Navigation System with Voice Assistance for Museums
In the current era of smart heterogenous devices, the surrounding environment too needs to be smarter to match the gravity of such devices. Such advanced environment can be built with the technology called Internet of Things (IoT). Due to the presence of such vivid thing devices in the Internet of Things (IoT) environment, the task of automatically predicting the end users desires can play an important role when it comes to match the pace of modern society with too much diverse aspects. Since last decade, people have deviated their attention towards Indian ancient culture and Museums are eye catching attraction where our ancient cultural heritage exist. To improvise the slow pace growth of the tourism sector, there is the crucial requirement of technological improvement especially due to the restrictions on installations of external hardware within the close proximity. One prominent way of improving tourists experience at museums is to renovate existing museums with IoT-based smart devices which is programmed such a way to automatically navigate the user indoor and briefs the associated information about artwork without any user intervention. In this paper, we propose an IoT-based smart indoor navigation system along with voice assistance which can enhance the tourists experience in a museum. In addition, the proposed design also delivers the very personalized cultural contents related to the visited artworks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Environmental Applications of Green Engineered Copper Nanoparticles
Naturally engineered nanomaterials in recent times have myriad potential in different fields. Moreover, green derived nanoparticles (NPs) encourage broader implementation for wider applications. Amongst many metals, copper and its oxide-based nanoparticles (CuONPs) have increased utmost consideration owing to its specific characteristics, abundance, and cost-effectiveness. Major setback of chem-ical and physical methods of synthesising CuONPs involves high cost along with environmental hazards. Aforementioned challenge compelled researchers to explore green synthesised CuONPs that is much cheaper, efficient, economically beneficial, non-toxic, and eco-friendly. Existing plant-based CuONPs have potential efficiency to enhance the toxic effects against the plant pathogens and combating environmental pollution through bioremediation. Several extracts of plant derivatives have been used for the synthesis of CuONPs such as Azadirachta indica, Hibiscus rosa-sinensis, Murraya koenigii, Moringa oleifera, Tamarindus indica, Eclipta prostrate, Olea europaea, etc. Microbes as cell factories are more efficiently used as NPs compared to larger plants such as, green algae Botryococcus braunii, brown algae Macrocystis pyrifera, Bifurcaria bifurcate etc. Bio-based CuONPs have been applied in numerous fields such as pharmaceutical, molecular biology, bioremediation, cosmetics, textiles etc. Several of them also employed in dye degradation, water treatment, food preser-vation, Photovoltaic devices, solar energy conversions, and field emission emitters. However, as in clinical setup due to their efficacy these are exclusively used as anti-cancer, antimicrobial agents. Further, their high antioxidant potential renders them as an invaluable tool for biomedical devices. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Fungal-based synthesis to generate nanoparticles for nanobioremediation
Nanotechnology has gained immense popularity with its innumerable biological agents, which are replacing toxic chemicals with an advanced technique for reducing and stabilizing nanoparticles (NPs). Fungal nanotechnology has represented exceptional technique in this area, owing to its nontoxicity, eco-friendly nature for fungal NPs, and nanostructure synthesis by reducing enzymes using either intracellular or extracellular techniques. Further, ease lies in the scale up-and downstream process owing to the increased surface area of the mycelial cells. Fungi and yeast are highly potential secretors of extracellular enzyme, grow fast, and are simple to maintain. Biogenic fungal NPs have been applied in the field of industry, agriculture, medicine, and other sectors too, and are used as bioremediators, drug delivery, biosensors, MRI, medical imaging, cancer therapy, etc. Mycoremediation can serve as a facilitator in bioremediating the toxins by immobilizing or inducing the synthesis of enzymes. Fungal NPs have shown an effective and efficient clean-up of the environment from the chemical pollutants and heavy metals, reducing total time consumption and total cost reduction. Fungal species of A. flavus and T. harzianum have shown promising crude oil degrading abilities with silver NPs at a very low concentration. Other fungal species used as resources for metal NPs that have been useful as bioremediators include Aspergillus, Fusarium, Penicillium, and Verticillium. The Author(s), 2023. All rights reserved. -
A Gated Recurrent Unit Based Continual Normalization Model for Arrythmia Classification Using ECG Signals
In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined MIT-BIB database to surpass the overfitting issue. Therefore, in the present research work, the Gated Recurrent Unit (GRU) based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify Electrocardiogram (ECG) signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed GRU based Continual Normalization technique obtained an accuracy of 99.41% which is better when compared with the existing researches. 2023 IEEE. -
Stock market prediction employing ensemble methods: the Nifty50 index
Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance
Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifier (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis
Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements. 2021. All Rights Reserved. -
Generalized Vertex Induced Connected Subsets of a Graph
Vol.2 (May), 61-68 -
On Πk – connectivity of some product graphs
Vol. 21, No.2, 70 - 79 ISSN 13105132 -
PBIB Designs and association schemes arising from some connected dominating sets
Vol.4 (5) : 225-232 !SSN 2252-1512 -
Detection and Classification of Potholes in Indian Roads Using Wavelet Based Energy Modules
Maintenance of roads is one the major challenge in the developed countries. The well maintained roads always indicates the economy of the whole country. The heavy use of roads, environmental conditions and maintenance is not performed regularly that leads the formation of potholes which causes the accidents and unwanted traffics. The paper discuss about the detection of potholes based on wavelet energy field. The proposed method mainly includes three phases (A)Wavelet energy filed is constructed in order to detect the image by using geometric criteria and morphological processing (B)Extracting Region of intersect by edge based segmentation technique (C)Classifying the potholes using Neural Network. 2019 IEEE. -
A Novel Approach for Detection and Recognition of Traffic Signs for Automatic Driver Assistance System Under Cluttered Background
Traffic sign detection and recognition is a core phase of Driver Assistance and Monitoring System. This paper focuses on the development of an intelligent driver assistance system there by achieving road safty. In this paper a novel system is proposed to detect and classify traffic signs such as warning and compulsory signs even for occluded and angular tilt images using Support Vector Machines. Exhaustive experiments are performed in order to demonstrate the efficiency of proposed method. 2019, Springer Nature Singapore Pte Ltd. -
Indian Road Lanes Detection Based on Regression and clustering using Video Processing Techniques
Detecting the road lanes from moving vehicle is a difficult and challenging task because of road lane markings with poor quality, occlusion created by traffic and poor road constructions. If the driver is not maintaining the road lanes properly, the proposed system detects the road lanes and gives the alarm to the driver so that driver can take the corrective actions there by we can avoid the accidents. The paper mainly focusses on detection of road lanes from sequence of image taken from the video from moving vehicle. The Methodology mainly consisting of lane segments merging and fitting using clustering and weighted regression techniques to fit the curve in the place of group of lane segments and curve fitting separately. 2021, Springer Nature Singapore Pte Ltd. -
The Design of Driver Fatigue Detection Based on Eye Blinking and Mouth Yawing
In modern era, the Intelligent Transportation System (ITS) is very essential for the betterment of transport management, autonomous vehicles and especially for safe driving. The statistics suggest that the major severe accidents occur because of drivers drowsiness. The main objective of this work is to give the alert alarm when the driver is falling asleep. In the proposed study, the driver's face is detected using the Viola Jones algorithm, and a novel approach to detecting eye blinks using template matching and a similarity measure. For effective eye tracking, the normalized correlation coefficient is calculated. The correlation score is used to identify eye blinks since a blink causes a significant change in the correlation score. In tracking of mouth yawing finding the darkest region between the lips. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Polypyrrole functionalized MoS2 for sensitive and simultaneous determination of heavy metal ions in water
Assessing heavy metal ion (HMI) contamination to sustain drinking water hygiene is a challenge. Conventional approaches are appealing for the detection of HMIs but electrochemical approaches can resolve the limitations of these approaches, such as tedious sample preparation, high cost, time consuming and the need for trained professionals. Here, an electrochemical approach is developed using a nano-sphered polypyrrole (PPy) functionalized with MoS2 (PPy/MoS2) by square wave anodic stripping voltammetry for the detection of HMIs. The developed sensor can detect Pb2+ with a limit of detection of 0.03 nM and a sensitivity of 36.42 ?A nM?1. Additionally, the PPy/MoS2 sensor was employed for the simultaneous detection of HMIs of Cd2+, Pb2+, Cu2+ and Hg2+. The reproducibility, stability and anti-interference studies confirm that the sensor can be used to monitor HMI contamination of water. 2025 The Royal Society of Chemistry. -
Molecular docking study, and ADMET analysis for the synthesized novel Zn(II) complexes as potential SARS-CoV-2 inhibitors
A new SARS-CoV-2 virus and its variants including omicron created a pandemic situation and caused more deaths in worldwide prompted many researchers to explore potential drug candidates. In this connection, we explored the first-of-its-kind report on computational studies such as molecular docking, and ADMET properties of Zn(II) complexes. The studies revealed the novel zinc complexes have high binding affinities with the SARS-CoV-2 spike glycoprotein (6vxx) alpha variant (7EKF), beta variant (7ekg), gamma variant (7EKC), delta variant (7V8B), and the omicron variant (7T9J). Molecular docking results of RMSD for SARS-CoV-2 beta variant (7ekg) and gamma variant (7EKC) are within excellent chemical stability in their protein-ligand complex state and should be effective in the biological system. ADME studies provided the better results with no adverse effect of toxicity related AMES along with absence of hepatotoxicity and skin sensitization when compared to Molnupiravir drug and it has a greater hepatotoxicity. This study could open further exploration of these novel zinc complexes for SARS-CoV-2 inhibition. (2024) DergiPark. -
Heat transfer enhancement in the boundary layer flow of hybrid nanofluids due to variable viscosity and natural convection
The aim of the current work is to explore how heat transfer can be enhanced by variations in the basic properties of fluids in the presence of free convection with the aid of suspended hybrid nanofluids. Also, the influence of the Laurentz force on the flow is considered. The mathematical equations are converted into a pair of self-similarity equations by applying appropriate transformations. The reduced similarity equivalences are then solved numerically by Runge-Kutta-Fehlberg 45 th -order method. To gain better perception of the problem, the flow and energy transfer characteristics are explored for distinct values of significant factors such as variable viscosity, convection, magnetic field, and volume fraction. The results acquired are in good agreement with previously published results. The noteworthy finding is that the thermal conductivity is greater in hybrid nanofluid than that of a regular nanofluid in the presence of specified factors. The boundary layer thickness of both hybrid nanofluid and normal nanofluid diminishes due to decrease in variable viscosity. The fluid flow and temperature of the hybrid nanofluid and normal nanofluid increases as there is a rise in volume fraction. 2019 -
3D flow and heat transfer of micropolar fluid suspended with mixture of nanoparticles (Ag-CuO/H2O) driven by an exponentially stretching surface
Purpose: The purpose of this paper is to discuss the 3D micropolar hybrid (Ag-CuO/H2O) nanofluid past rapid moving surface, where porous medium has been considered. Design/methodology/approach: The model of problem was represented by highly partial differential equations which were deduced by using suitable approximations (boundary layer). Then, the governing model was converted into five combined ordinary differential equations applying proper similarity transformations. Therefore, the eminent iterative RungeKuttaFehlberg method (RKF45) has been applied to solve the resulting equations. Findings: Higher values of vortex viscosity, spin gradient viscosity and micro-inertia density parameters are reduced in horizontal direction, whereas opposite behaviour is noticed for vertical direction. Originality/value: The work has not been done in the area of hybrid micropolar nanofluid. Hence, this article culminates to probe how to improve the thermal conduction and fluid flow in 3D boundary layer flow of micropolar mixture of nanoparticles driven by rapidly moving plate with convective boundary condition. 2020, Emerald Publishing Limited.