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Predictive Modeling for Uber Ride Cancellation and Price Estimation: An Integrated Approach
In the realm of ridesharing services, exemplified by Uber, two formidable challenges have surfaced: ride cancellations and precise fare estimation. This research introduces an innovative, integrated approach that leverages predictive modeling to address both issues. By analyzing historical ride data, we identify the intricate factors influencing cancellations, and through machine learning techniques, we develop predictive models to forecast cancellation likelihood. Additionally, we pioneer a dynamic approach to fare estimation by considering historical data alongside real-time variables. By unifying these strategies, we aim to enhance user satisfaction, optimize driver allocation, and promote trust and transparency within the ridesharing ecosystem. 2024 IEEE. -
Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India
As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics. 2024 IEEE. -
Predictive analytics in cryptocurrency using neural networks: A comparative study
This paper is concerned with assessing different neural network based predictive models. Each of these predictive models has one goal and that is to predict the price of a cryptocurrency, Bitcoin is the cryptocurrency taken into consideration. The models will be focusing on predicting the USD equivalent value of bitcoin using historical data and live data. The neural network models being assessed are a Convolutional Neural Network, and two variations of the Recurrent Neural Network that are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The goal is to observe the validation loss of each model and also the time it takes to train or epoch for each training set which basically just determine its efficiency and performance. The results that are achieved are almost what was expected as LSTM outperforms CNN but the when we take a look at GRU, it is at par with LSTM. However, CNN is quicker at training or creating epochs and the validation loss is acceptable and not too high but it looks so when it is compared with the Recurrent Neural Networks such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). BEIESP. -
Predictive Analytics for Stock Market Trends using Machine Learning
Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE. -
Predictive Analytics for Network Traffic Management
It examines how this can be applied to monitoring network traffic and carrying out predictive analysis to improve the functionality and effectiveness of network management. The study uses historical data of the network traffics and uses machine learning techniques such as the Long Short Term Memory based models and the Ensemble Methods to predict the traffic patterns in the future. It includes data gathering, data pre-processing, feature selection, model choice, model training, model validation, and the architectural setup of the machine learning solution in a real-time stream processing pipeline using Apache Kafka and Apache Flink. It is evident from the results that the proposed models yield a high level of accuracy in terms of prediction and that the Ensemble method alone gives a slightly higher accuracy than LSTM in the specific metrics. Real-time values closely followed actual traffic level, thus allowing real-time adjustments in network usage. In light of this, there is a clear understanding of the significance of having reliable data preprocessing, feature engineering, and model optimization process. The study also notes the need in prediction concerning data quality and scalability issues taking into account that current and future networks are characterized as dynamic and highly complex to offer more effective solutions for intelligent and proactive networking. 2024 IEEE. -
Predictive analytics for cardiac arrhythmia using machine intelligence
Myocardial Infarction (MI) is the primary cause of death worldwide. MI occurs when a plaque buildup in the inner surface of the coronary artery suddenly ruptures and prevents the blood flow. A heart attack is medically termed as MI. It is the irreversible damage caused by the prolonged ischemia. Ischemia is nothing but the heart organ doesn t get enough blood and oxygen which is also termed as coronary artery disease or coronary heart disease. The heart gets damaged if it has not received enough blood or oxygen. In connection to the damage of the heart, arrhythmia would occur. Arrhythmia is the problem based on the heart rhythm or rate of the heartbeat. Tachycardia, when there is a fast beat in the heart. Bradycardia, when the heart beats too slow. The common type of arrhythmia is atrial fibrillation. The great concern is that the patient who has arrhythmia has to be treated immediately. They lose consciousness in a few minutes when the heart is not pumping enough blood mainly to the brain. Death occurs when the patient is not given emergency treatment. newline Treatment which is included in the emergency is defibrillation and Cardiopulmonary Resuscitation (CPR). CPR is an emergency procedure which is combined with the chest compressions. It is through artificial ventilation which gives manual effort, preserves the brain functions until further treatment for the restoration of spontaneous blood circulation. The common symptoms of sudden cardiac death are chest pain, shortness of breath, severe wheezing, irregular heartbeats, fainting etc. newlineHeart Scar tissue which is not like heart muscle. It doesn t contrast like the normal heart muscle. Heart muscles get damaged for the heart attack patient based on the time of the treatment. The damage of the heart is based on the blockage of the artery. Arrhythmia can be predicted based on the volume of the scar region in the heart. Arrhythmia patients are treated by fixing Implantable Cardioverter Defibrillator (ICD). -
Predictive analytics for cardiac arrhythmia using machine intelligence
Myocardial Infarction (MI) is the primary cause of death worldwide. MI occurs when a plaque buildup in the inner surface of the coronary artery suddenly ruptures and prevents the blood flow. A heart attack is medically termed as MI. It is the irreversible damage caused by the prolonged ischemia. Ischemia is nothing but the heart organ doesn’t get enough blood and oxygen which is also termed as coronary artery disease or coronary
heart disease. The heart gets damaged if it has not received enough blood or oxygen. In connection to the damage of the heart, arrhythmia would occur. Arrhythmia is the problem based on the heart rhythm or rate of the heartbeat. Tachycardia, when there is a fast beat in the heart. Bradycardia, when the heart beats too slow. The common type of arrhythmia is atrial fibrillation. The great concern is that the patient who has arrhythmia has to be treated immediately. They lose consciousness in a few minutes when the heart is not pumping enough blood mainly to the brain. Death occurs when the patient is not given emergency treatment. -
Predictive Analysis of the Recovery Rate from Coronavirus (COVID-19)
Estimation of recovery rate of COVID-19 positive persons is significant to measure the severity of the disease for mankind. In this work, prediction of the recovery rate is estimated based on machine learning technology. Standard data set of Kaggle has been used for experimental purpose, and the data sets of COVID cases in Italy, China and India for these countries are considered. Based on that data set and the present scenario, the proposed technique predicts the recovery rate. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predictive analysis of stock prices through scikit-learn: Machine learning in python
Scikit-learn, a tool for developing machine learning algorithms, is a standard library of python. Through Scikit-learn, a trained model for predictive analysis can be developed. Such models aim to provide accurate predictions. Stock predictions are based on changes and patterns identified in the historical dataset. Following the trends and patterns of the historical changes of stocks, machine learning algorithms can be developed for achieving accurate outcomes. An effective model is developed, which enhance the working pattern or performance of the machine that further helps to draw a precise analysis of stocks. 2023 Scrivener Publishing LLC. -
Predictive Analysis of Sleep Disorders Using Machine Learning: A Comprehensive Analysis
The diagnosis of sleep disorders often relies on subjective patient reports, sleep diaries, and potentially cumbersome polysomnography (PSG) tests. However, these methods have limitations such as subjectivity, sleep diaries require meticulous effort, and expensive PSG tests are expensive, resource-intensive, and may not accurately capture sleep patterns in a non-clinical setting. Sleep disorders pose significant health risks and can impair overall well-being. Predictive analysis plays a crucial role in identifying individuals at risk of developing sleep disorders, enabling timely interventions and personalized treatment plans. In this paper, a comparative analysis of regression and classification models for sleep disorders prediction using machine learning (ML) techniques on insomnia and sleep apnea are discussed. Through extensive experimentation and comparative analysis, XGBoost and AdaBoost demonstrated as the most effective predictive models for insomnia and sleep apnea. AdaBoost and XGBoost classifiers are displaying 93.49% and 92.73% respectively. It is therefore possible to draw the conclusion that AdaBoost and XGBoost are doing well based on the findings as a whole, as indicated by the results. Our findings contribute to advancing the understanding and application of ML techniques in sleep disorder prediction, paving the way for more accurate and timely diagnosis based on ML techniques and personalized interventions in clinical practices. 2024 IEEE. -
Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach
The number of possibilities to analyze educational data using data mining techniques is expanding, with the goal of improving learning outcomes. There is an explosion in data produced by online and virtual education, e-learning platforms, and institutional IT. Using these statistics, teachers could gain valuable insights into their students' learning habits. Academic performance of students and other useful information can be analyzed with the help of educational data mining. Model training consists of three primary steps: data preprocessing, feature selection, and training the model. To eliminate unwanted problems like noise and redundant attributes, data preparation is necessary. By prioritizing which features to calculate, the mRMR algorithm lowers calculation costs. Feature selection plays a crucial role in training A-CNN-BiLSTM models. The suggested approach routinely outperforms BiLSTM and CNN, two state-of-the-art algorithms. With a data accuracy percentage of 96.57%, it's clear that there was a significant improvement. 2024 IEEE. -
Predictive analysis in smart agriculture
Analyzing large databases for hidden connections, correlations and insights is known as big data analytics. Although many countries still use outdated farming methods, technological advancements have allowed for specific improvements (especially in developing countries). Big data analytics has the potential to expand the agricultural sector in this regard significantly. The farmers rely heavily on old methods for deciding what to plant and how to cultivate it. Walking through fields, selecting soil samples for moisture analysis, and visually inspecting plant leaves are typical examples of these time-honored practices. Understanding the significance of technology for acquiring crop information in considerable amounts and turning that data into usable knowledge is crucial for agriculturists (mainly farmers). Integration of big data could help agriculture make changes to its current practices. If used correctly, big data analytics can shed light on the most efficient crop cultivation methods. Extensive developments in three areas-crop prediction, precision farming and seed production-are reshaping the agricultural industry. There are four parts to this chapter. The first part of this paper provides an introduction to analytics on big data in agriculture. The second part will then focus on the various big data methods used in the agricultural sector. The third section provides two examples of how big data analysis methods were put to use in the field of agriculture. In the fourth section, the authors examine the several agricultural research avenues open to scholars and scientists. This chapter concludes with a brief overview. 2023 River Publishers. All rights reserved. -
Prediction of Users Behavior on the Social Media Using XGBRegressor
The previous decennium has seen the growth and advance with respect to social media and such that has violently also immensely expanded to infiltrate each side of user lives. In addition, mobile network empowers clients to admittance to MSNs at whenever, anyplace, for any character, including job and gathering. Accordingly, the association practices among clients and MSNs are getting completer and more confounded. The goal of this paper is to examine the number of followers, likes, and post for Instagram users. The dataset yielded several fundamental features, which were used to create the model with multimedia social networks (MSNs). Then, natural language processing (NLP) features should be added and finally incorporate features derived in distinction to a machine learning technique like XGBRegressor with TF-IDF technique. We use two performance indicators to compare the different models: root mean square error (RMSE) and the R2 value. We achieved average accuracy using XGBRegressor which is 82%. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Prediction of the capture and utilization of atmospheric acidic gases by azo-based square-pillared fluorinated MOFs
More than the permissible limit of acidic gases like CO2, SO2, and NO2 in the atmosphere are responsible for the formation of acid rain, the greenhouse effect and many other undesirable environmental hazards. So, the capture and utilization of these gases are essential for mankind. Herein, we proposed an azo-based square pillared MOF, [Ni(MF5)(1,2-bis(4-pyridy)diazene)2]n, with the CUS metal site, i.e. M = Al/Fe, for the selective capture and conversion of acidic gas molecules into commodity chemicals such as cyclic carbonate, sulphite and nitrite. With the aid of Density Functional Theory (DFT), [Ni(MF5)(1,2-bis(4-pyridy)diazene)2]n has been optimized, and the specific force field is derived via guest-host interaction. The Grand Canonical Monte Carlo (GCMC) simulation has been used to explore the guest-host interactions over a wide range of pressures, and their respective stability under pre-humidification is evaluated. The adsorption prediction reveals that MFFIVE-Ni-apy have a higher adsorptive capacity (37.1 mmol g?1), and especially ALFFIVE-Ni-apy possesses a higher affinity towards guest molecules (CO2, SO2) rather than FEFFIVE-Ni-apy. Additionally, the adsorption of gases in the presence of humidity reveals that ALFFIVE-Ni-apy has an optimal adsorption capacity for all investigated acidic gases even at 38.5 RH%. The absorbed acidic gases on MFFIVE-Ni-apy were used for the theoretical investigations on cycloaddition with the aid of DFT as an application perspective of the toxic gases instead of expelling into atmosphere. The Climbing Image Nudged Elastic Band (CI-NEB) approach was used to discover the transition state in this scenario, in which the cycloaddition of adsorbed CO2, SO2, and NO2 gases with epoxides leads to the formation of cyclic carbonates, sulphites, and nitrates, respectively. 2023 The Royal Society of Chemistry. -
Prediction of Stock Prices using Prophet Model with Hyperparameters tuning
As part of the data analytical process, predicting and time - series are crucial. In academics and financial research, anticipating share prices is a prominent and significant subject. A share market would be an uncontrolled environment for anticipating shares since there are no fundamental guidelines for evaluating or anticipating share prices there. As a result, forecasting share prices is a difficult time-series issue. fundamental, technical, time series predictions and analytical strategies are just a few of the various techniques and approaches that machine learning uses to execute stock value predictions. This article implements the stock price prediction, Researchers compared the model of the prophet with the tuned model of the prophet. By utilizing the tuning of hyperparameters using parameter grid search to improve the performance of the model accuracy for the best prediction. The findings of the study demonstrated that tuned model of the prophet with hyperparameters tuning which results in model accuracy and based on the experimental findings mean squared error (MSE) and mean absolute percentage error (MAPE) has significant improvement. 2022 IEEE. -
Prediction of stock market price using hybrid of wavelet transform and artificial neural network /
Indian Journal of Science and Technology, Vol.9, Issue 8, pp.1-5, ISSN: 0974-5645 (Online) 0974-6846 (Print). -
Prediction of stock market price using hybrid of wavelet transform and artificial neural network
Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System. -
Prediction of software defects using object-oriented metrics
In recent years, many of the object-oriented software metrics were proposed for increasing the quality of software design such as prediction of defects and the maintainability of classes and methods. As the word metrics is frequently used for specific measurements taken on a particular process or item and in object-oriented metrics the metrics are the unit of measurements that is used to characterize the data.The fundamental point of this research is to identify the significance difference between software metrics which observes defect prediction and also study about their relation involving in the object oriented metrics that is named as "Chidamber and Kemerer metric suite" which is also known as "CK metrics suite", the number of defects and then finally decide the differences of the metrics in ordering to Eclipse classes as defective and selected with regard to defect prediction. IAEME Publication. -
Prediction of Rainfall Using Seasonal Auto Regressive Integrated Moving Average and Transductive Long Short-Term Model
One of the most crucial parts of the practical application in recent years has been the analysis of time series data for forecasting. Because of the extreme climate variations, it is now harder than ever to estimate rainfall accurately. It is possible to forecast rainfall using a number of time series models that uncover hidden patterns in past meteorological data. Choosing the right Time Series Analysis Models for predicting is a challenging task. This study suggests using a Seasonal Auto Regressive Integrated Moving Average (SARIMA) to forecast values that are similar to historical values that exhibit seasonal patterns. Twelve years of historical weather data for the city of Lahore (from 2005 to 2017) and Blora Regency are taken into account for the prediction. The dataset underwent pre-processing operations like cleaning and normalisation before to the classification procedure. For classification, Transductive Long Short-Term Model (TLSTM) is employed which has learned the dependency values where the memory blocks are recurring and capable of learning long-term dependencies on this model. Further, TLSTM's goal is to increase accuracy close to the test point, where test points are selected as a validation group. The performance of the models has been assessed based on accuracy (99%), precision (98%), recall (96%) and fl-score (98%). Proposed SARIMA model showed optimistic results when compared to existing models. 2023 IEEE. -
Prediction of Material Removal Rate and Surface Roughness in Hot Air Assisted Hybrid Machining on Soda-Lime-Silica Glass using Regression Analysis and Artificial Neural Network
Hybrid machining is a combination of conventional with the non-conventional process or two non-conventional processes. In the present work, an attempt has been made to combine hot air with a conventional cutting tool to form a novel Hot Air Assisted Hybrid Machining (HAAHM) for the machining of soda-lime-silica glass. The mathematical model for the Material Removal Rate (MRR) and Surface Roughness (Ra) using Regression Analysis (RA) and the Artificial Neural Network (ANN) models has been developed for the grooving process. The deviation of 8.24% and 7.70% were found in the prediction of MRR and Ra by regression analysis and the deviation of 1.89% and 1.70% for MRR and Ra using an artificial neural network model. The deviation between the predicted and the experimental results of both the models are found to be within the permissible limit. Higher predictive capabilities were observed in ANN model than the regression model. However, both models demonstrated good agreement with the MRR of soda-lime-silica glass by this hybrid machining process. 2020, Springer Nature B.V.