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Prediction of football players performance using machine learning and deep learning algorithms
In modern days the margin of error for football game is low, therefore the ultimate aim of the game is to win the match. The performance of the players in the match affects the results of the game. Due to this it is very important to evaluate the player and know his weakness. Manual evaluation tends to generate many errors and take more time. In the current research the statistical model is proposed to predict the stats of the football player based on previous session data by considering various aspects of the game. Through literature reviews it is observed that machine learning and deep learning algorithms can be used predict the performance of football player. But which model would be more efficient considering the positions of the player is not considered in any article. The proposed model has designed separate model as per the position of the player during the game. This can help to predict the player's performance as per their playing position. The current study has successfully implemented various machine learning and deep learning models and provide comparative analysis of the same. Each position has considered different variables associated with that position. The performance of these models is compared for further clarification 2021 IEEE. -
Mapping Cyclone and Flood Hazard Vulnerability in Puri District, Odisha, India, Using Geoinformatics
India is vulnerable to many natural and human-made disasters due to its unique geo-climatic and socio-economic conditions. This paper focuses on natural disasters; such as cyclones and floods in the Puri district in the Indian state of Odisha. In this study, a number of floods and cyclones that occurred in the district were identified. The thematic maps of the influencing factors such as soil type, flood and cyclone vulnerability, elevation, and 2020 land cover were created using ArcGIS 10.3. Thereafter, the weighted overlay method was adopted based on analytical hierarchy process (AHP) to map the overall vulnerability of the district. The results derived from this study exhibited that the district is highly vulnerable to floods and cyclones. Finally, strategies were recommended for hazard risk reduction covering enhancing awareness towards hazards, improving early warning systems, establishing better communication between various stakeholders, and strengthening environmental protection and disaster risk reduction. Furthermore, measures for mitigation such as creating shelters, post-disaster rehabilitation, better and improved health facilities, incorporating green infrastructure at critical locations, relying on nature-based measures, execution of mangrove plantation along the coastal belt of the district, creating barriers or dykes to prevent water tides, and plummeting leachate due to improper waste disposal near the coast are suggested. The analysis and mapping of hazard vulnerability can act as a reference for urban planners and policymakers to promote Sustainable Development Goal (SDG) number 11 which is sustainable cities and communities. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Developmental Regulations to Conserve Catchment Area of an Urban Water Body: A Case of Upper Lake in the City of Bhopal, Madhya Pradesh, India
A lake reflects its catchment and provides a wide range of ecological services useful for the sustenance and betterment of human lives and other living organisms. The fast-paced developmental activities in the catchment area of lakes and over-consumption of available resources are resulting in the degradation of ecological services of the lake. The Upper Lake in Bhopal, a million-plus population city situated in the central part of India, has been selected for the study. The current regulatory regime for environmental conservation of the catchment area comprises a legislative framework and a set of regulatory institutions. Inadequacies in each accelerates environmental degradation of the Upper Lake, delays and escalates cost of developmental projects. This paper is an attempt to find out gaps in the existing institutional framework to conserve the catchment area. The present study focused on identifying and assessing major issues pertaining to the degradation of the Upper Lake, thereafter establishing a cause-and-effect relationship between anthropogenic activities and their environmental impacts. Subsequently, formulating developmental regulations forconserving theUpper Lake and its catchment area. Primarily, GIS application has been employed to delineate the catchment area of the Upper Lake by using catchment area assessment technique. Furthermore, a primary survey targeting the village stakeholders in the catchment area was conducted to analyze socio-economic profile and assess the agricultural practices adopted. Finally, formulated an institutional framework to conserve, govern and monitor the catchment area and the Upper Lake from the anthropogenic issues. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Investigating and analyzing the causality amid tourism, environment, economy, energy consumption, and carbon emissions using TodaYamamoto approach for Himachal Pradesh, India
Himachal Pradesh is a preferred tourist destination with a Compound Annual Growth Rate (CAGR) of 10.76% between 201112 and 202021. The increasing trend of CAGR has boosted the tourism economy in the state while impacting the local environment. The negative impacts have recently increased due to changes in climatic patterns and increased tourism influx during the post-pandemic period. In this context, the present study analyzed the impact of tourism on the environment, economy, and energy consumption using the Environmental Kuznets Curve (EKC) hypothesis. The novelty of this study is to the existing literature on sustainable tourism development through investigating the interrelationship between tourism, environment, economy, energy consumption, and carbon emissions by employing the TodaYamamoto (TY) technique. This study will be a pioneering scientific investigation with quantitative results in the western Himalayan states of India, encompassing Jammu & Kashmir, Uttarakhand, and Himachal Pradesh. The annual data for each variable, such as per capita carbon emission (CEP), per capita Gross State Domestic Product (GSDP), per capita GSDP square, per capita energy consumption (ECP), and per capita tourism receipts (TRP), was collected from 2010 to 2021. This study exhibited an inverted-U EKC in the state, signifying the initial stage of economic development and extensive exploitation of natural resources for tourism. The TY results indicated an inter-causal relationship and feedback association among the variables in the study area. Thus, increased TRP would lead to an upsurge in energy consumption affecting the environmental quality due to increased carbon emissions. Such environmental degradation in the state would negatively impact the tourism sector in the long run. The research findings would guide planners and policymakers in promoting sustainable tourism. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Mental health professionals insights on developing and implementing a Mental Health Awareness and Destigmatisation program (MHAD) for adolescents
This study examined mental health professionals insights on developing and implementing a Mental Health Awareness and Destigmatisation (MHAD) program for adolescents aged 1418 years in Bangalore. Qualitative Interviews with 17 professionals revealed three main themes: 1. Awareness and Destigmatisation Programs: A Boon 2. Key Ingredients: Program Content and Delivery Style 3. Shaping Program Success: Key Drivers and Challenges. Professionals recommend interactive programs that promote open discussions and educate them on symptom recognition, online behavior, and healthy relationships. They identified key enablers and challenges, emphasizing the programs ability to empower adolescents, parents, and educators to create a supportive, stigma-free environment. 2024 Taylor & Francis Group, LLC. -
Knowledge, Attitude, and Stigma Among Adolescents: Effect of Mental Health Awareness and Destigmatisation (MHAD) Program
Background: Stigma against mental health problems is a common issue for adolescents aged 1418 years. However, comprehensive programs that simultaneously address awareness and stigma reduction tailored to the specific needs of this age group are lacking. Method: This study investigated the effectiveness of the Mental Health Awareness and Destigmatisation Program (MHAD) in reducing stigma and improving knowledge and attitudes towards peers with mental health problems. A quasi-experimental pre-post design was employed among adolescents aged 1418 years from an educational institution in Bangalore. After excluding those with high baseline mental health symptoms (PSC-17 > 20), a preassessment was conducted on adolescents' knowledge, attitude, and stigma (n = 52) using the Mental Health Knowledge Schedule, Self-structured Case Vignettes, and Peer Mental Health Stigmatization Scale. After completing the 6-week program, three participants were excluded from the post-assessment, as their attendance was less than 50%. A total of 49 (mean age = 16 years) adolescents were included in the post-assessment. Results: The paired sample t-test revealed significant improvements in all stigma scores. The Wilcoxon signed-rank test indicated a significant improvement in Recognition of Mental Illness scores. Conclusion: Findings showed that MHAD, an education-based program, was effective in reducing adolescents' stigma towards peers with mental health problems and improving their overall recognition of mental health symptoms. Research across larger adolescent populations is essential to enhance these interventions' long-term impact and sustainability. By closely monitoring and expanding research efforts, we can gain deeper insights into how these programs foster self-awareness, a crucial factor in recognizing mental health needs, challenging stigma, and promoting help-seeking behaviors among adolescents. 2024 Wiley Periodicals LLC. -
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.