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Prediction of CFRP and NSM-Wrapped Composite Column Capacities Using Experimentation and an Ensemble Machine-Learning Approach with SHAP Interpretation
Column capacity is an essential parameter in structural design, and its accurate determination is critical for a safe load-Transfer mechanism in structures. Also, experimental and accurate model-based assessments are critical to the column capacity evaluation. The main objective of this study is to experimentally and analytically investigate near-surface mounted (NSM) wrapped columns of different configurations and compare their capacity enhancements with the capacity enhancements of carbon fiber-reinforced polymer (CFRP). The study is also focused on developing a statistical regression model and extreme gradient boost (XG Boost), an ensemble machine-learning (ML) approach-based model, and examining both models developed for the experimental results by Shapley additive explanations (SHAP) interpretations. Therefore, the study experimentally reviewed the behavior of 24 composite columns to gain insights into experimental and code-recommended column capacities, stress-strain responses, axial stiffness, ductility factors, and failure modes. NSM-wrapped columns gained 10% strength increments, and, in comparison, the full-wrapped CFRP columns achieved 22% strength enhancement. The structural columns in a structure typically require various levels or types of strengthening, depending on their loading conditions, geometry, and material properties. With a 10% increment, the NSM technique suits columns needing lesser strength enhancements. Therefore, a key finding of the study is that the contribution of NSM longitudinal wrapping to column capacity is significant and cannot be ignored. A statistical regression model is developed for column capacity with four key parameters: percentage steel reinforcement, the extent of epoxy adhesion, the weight of the specimen, and the concrete clear cover. A model based on XG Boost, an ensemble ML approach, is also developed for the same four key parameters. The models developed are evaluated by SHAP interpretations. The SHAP analysis technique interpreted this improved model for various input-output features. The XG Boost machine-learning algorithm, developed with a coefficient of determination of 0.99, is found to be a refined regression model. Also, the study establishes that the ensemble ML approach used in tandem with SHAP analysis is a robust prediction and model interpretation tool, highlighting the significance of the percentage of steel reinforcement and the extent of epoxy adhesion over the other variables for the experimental dataset. 2026 American Society of Civil Engineers. -
Prediction of CO and NOx Emission from Gas Turbine Using Machine Learning
In gas-turbine-based power plants, predictive emission monitoring systems (PEMS) are used to validate and back up the expensive continuous emission monitoring systems. Increasing energy consumption increased deforestation and carbon and flue gas emissions, harming the environment. The availability of relevant and ecologically sound data is crucial to their successful deployment. In this article, we adopted the Gas Turbine CO and NOx Emission Data Set Data Set from UCI machine learning repository to predict the CO And NOx emission from gas turbine using machine learning (ML). We developed the model using random forest and support vector algorithms. The random forest algorithm performs better for the data. 2025 Author(s). -
Prediction of Crime Hotspots Using Machine-Learning Techniques
Crime prediction is critical in improving police strategies and implementing measures for crime prevention and control. In recent years, machine learning has emerged as a critical way to predictive analytics in this domain. However, few studies have thoroughly compared various machine-learning algorithms for crime prediction. This study investigates the predicting capacities of various machine learning and ensemble approaches using historical public property crime data from a large city in India. Five ensemble models, Random Forest, AdaBoost, CatBoost, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) and Four machine learning models, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Nae Bayes and Decision Trees are used for crime predictive analysis in this study. The XGBoost model outperformed the other models tested, based primarily on historical crime data. XGBoost being an ensemble approachcombines multiple weak classifiers to create an effective classifier. Every weak learner concentrates on the faults made by the preceding ones, enabling the model to refine its predictions and fix errors repeatedly. When compared with other models used in the study, this resultedin higher accuracy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
Integrating cutting-edge technology with conventional farming practices has been dubbed smart agriculture or the agricultural internet of things. Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks. 2023, Springer Nature Limited. -
Prediction of Depression in Young Adults Using Supervised Learning Algorithm
Over the years, mental health has achieved an essential role in the pertinent development of a human being, and a large part of the population is affected by it. The most commonly affected community being college-going students, and the most common disorders being Anxiety and Depression. Depression is a leading cause of suicide in individuals, where suicide is the second most prevailing reason for death among 1529-year-olds. This study aims to identify the different reasons and other factors associated with depression to predict and determine whether an individual faces depressive disorders. For this research purpose, the most appropriate classifier is selected. The absolute accuracy of the proposed model is 91.17%, i.e., the model can correctly predict whether an individual has depression 91.17% of the time. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Prediction of Facial Emotions using Deep Learning and Machine Learning Techniques
Facial expression prediction has gained considerable attention in recent years, particularly because of its applications in human-computer interaction. This paper compares a wide range of deep learning and machine learning models for the prediction of face emotion using the CK+ dataset proposed by Cohn-Kanade. The dataset is characterized by seven classes of emotions represented by the labels, namely surprise, happiness, disgust, anger, sadness, fear, and contempt, on 784 training, 98 validation, and 99 testing images. To further improve model performance, preprocessing techniques were employed that enhanced data efficiency. To increase variability in the data and reduce overfitting, all images were scaled to a 48*48 pixel resolution, pixel values were scaled to be between 0 and 1 for uniformity and the following data augmentation techniques were implemented: 10-degree rotation, horizontal flip, 0.15 zoom. The five models that were tested were CNN, SVM, VGG16, InceptionV3 and VGG19. The results demonstrate the high accuracy achieved by the CNN model which showed an accuracy of 98.98%, 99% and 99% in training, validation and test respectively. The SVM classifier got an accuracy of 99%. Both InceptionV3 and VGG19 on the other hand achieved competitive testing accuracy values of 90.91% and 97.98% respectively, while VGG16 got tested and reached an accuracy of 85.86%. 2025 IEEE. -
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. -
Prediction of Friction Stir Welding Parameters Using Response Surface Methodology
The Friction Stir Welding (FSW) technique results in mixing and densification of weld joint in a more accurate and localized manner. FSW has been used to create a more significant weld with more structural integrity. In this research work, to join AA 3103 and AA 7075 was carried out. These alloys were preferred due to their wide variety of applications varying from aluminium fabrication to the aerospace industry. AA 7075, being a costlier metal, can be partially replaced with AA 3103, which can be economically justifiable for this research. The study tries to reveal the regression model by considering the FSW parameters like speed feed and offset. Various mechanical tests, impact tests and hardness tests were used for determining the most suitable weld joint. After conducting the tests, the results were analyzed using Minitab 18 software. The mathematical equations were derived out of Response Surface Methodology, which proved to be efficient. The report thus discusses the details in the analysis and study of FSW. 2023 American Institute of Physics Inc.. All rights reserved. -
Prediction of Grandiose Narcissism Using Machine Learning
The Gen-Zs have the tendency to exhibit a sense of self-importance and superiority excessively over social media. This study intends to predict Grandiose Narcissism based on Instagram usage and Fear of Missing Out (FoMO) among young adults. The study was conducted on a sample size of 300 young adults, recruited using convenient sampling, residing in the state of Assam, India. This study employed various machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNNs), and Gaussian Naive Bayes, to analyse the predictors of Grandiose Narcissism. Results showed that machine learning algorithms, especially KNN (90.7%) and Random Forest (88.70%) predicted Grandiose Narcissism accurately based onFoMO, Self-Esteem, PAUM. Additionally, Area Under Curve (AUC) in the range of 0.850.91 indicated that the variables in the data set are being discriminated in the context of specificity and sensitivity thoroughly. Significant influence of grandiose narcissism and FoMO on Instagram usage highlighted the role of social validation in enhancing online engagement. Future studies can include these algorithms to deduce patterns and develop real timebots to provide psychologically safe online environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prediction of ground water quality in western regions of Tamilnadu using LSTM network
Assessing and safeguarding groundwater quality is critical for sustaining life in water-scarce regions like western Tamil Nadu. The motivation behind this study stems from the pressing need to address water quality challenges in a region grappling with scarcity. Despite existing efforts, a notable research gap exists in predictive tools that comprehensively capture the nuanced temporal variations and trends in groundwater quality. This is where the LSTM network steps in, showcasing exceptional accuracy in short-term predictions and discerning long-term trends. This research uses Long Short-Term Memory (LSTM) networks, a variant of recurrent neural networks, to predict groundwater quality in South Indian Regions, especially in Tamil Nadu. Extensive data, encompassing parameters such as pH, dissolved oxygen, turbidity, and various chemical constituents, were gathered over an extended timeframe. The LSTM model was then trained on this historical dataset, factoring in temporal dependencies and seasonality inherent in groundwater quality data. The validation process rigorously tests the LSTM model against actual groundwater quality measurements. The results were impressive, as the model demonstrated a remarkable ability to unravel the complex variations in groundwater quality. 2024 Elsevier B.V. -
Prediction of Hazardous Asteroids Using Machine Learning
As the need for early detection and mitigation of potential threats from near-Earth objects continues to grow, this study presents a comprehensive approach to predicting hazardous asteroids through the application of machine learning techniques. With the increasing interest in safeguarding our planet from potential impact events, the accurate classification and prediction of hazardous asteroids is of paramount importance. This research leverages a diverse dataset comprising a wide array of asteroid characteristics, including orbital parameters, physical properties, and historical impact data, to train and validate machine learning models. The study employs a combination of feature engineering, data preprocessing, and state-of-the-art machine learning algorithms to assess the risk posed by asteroids in near-Earth space. 2024 IEEE. -
Prediction of health insurance premium using bidirectional long short-term memory network with local interpretable model-agnostic explanations
This research proposes an application of deep learning techniques towards the prediction of insurance premiums using ConvLSTM, BI-LSTM, and CNN-LSTM models. Nowadays, Insurance is becoming more sophisticated, there is a need for better models that predict premiums so that risk factors that can be properly valued. The aim of this study is to improve the accuracy and reliability of insurance premium prediction using deep learning methods. The main challenge is the shallow traditional models, whose capturing of temporal dependencies is ineffective and results are not explainable resulting in very few stakeholders having any trust to the predictions. To solve this, this study compared three models: ConvLSTM model, BI-LSTM and CNN LSTM. Of these, the BI-LSTM model was the most effective because it was able to learn bidirectional sequential patterns. These patterns were enhanced using L2 regularization, dropout and dense layers to improve generalization. The dataset used comes from a Kaggle repository, which contained actual insurance data incorporating age, BMI, region and smoking as attributes. Results showed that BI-LSTM had performed the best as compare to other models in terms of accuracy and loss minimization. Important findings highlighted features such as age, smoking, and BMI as pivotal to estimating premiums. Also, to make the model explainable, we incorporated Explainable AI using LIME which delivers interpretable explanations by showing and visualizing the most important features for single predictions. 2026 selection and editorial matter, K. V. Sambasivarao, and Anasuya Sesha Roopa Devi Bhima; individual chapters, the contributors. All rights reserved. -
Prediction of heart disease using XGB classifier
Predicting heart disease in advance could be a significant medical breakthrough because it is widespread. A reliable strategy that can be utilized to do this is machine learning. Decision tree classifiers, random forests, and multilayer perceptron have all been used in studies to predict heart disease. However, several of these techniques could be improved, like poor precision. In our research, we have taken the South African heart Disease dataset and implemented a few models, which include Support Vector Machine (SVM), K Neighbors (KNN), Artificial neural network and XG Boost Classifier. We have used different methods for measuring performance. SVM with 69.0 accuracy, KNN with 86.0 accuracy, and ANN with 80.0 accuracy. However, the XGB classifier has shown some promising results in predicting heart disease with an accuracy of 90%. Further, when the hyperparameters were tuned using the random search method, the accuracy increased to 92.8%. The benefit of this work is that it uses machine-learning approaches to enhance the performance of coronary heart disease prediction. 2024 Author(s). -
Prediction of indoor air circulation of residential room with adaptation of solar chimney using numerical technique
With the exponential increase in consumption of electrical power during the summer season by household, there is a great need for households to withhold sustainability. To maintain the temperature of the household a passive heating and cooling system is used i.e. Solar Chimney. Ventilation, through a natural convection process, is gaining a lot of attention to be an alternative technique for mechanical air conditioning ventilation because of its reduced power usage when compared to the external cooling devices used in residential buildings of hot regions. The present study, involve solar chimney of horizontal and vertical designs in comparison with different width and height. The following paper studies the effect of a solar chimney on the indoor thermal behavior using Numerical Technique for a prototype of a residential room. The performance on the ventilation velocity and air temperature operation inside the room with varying air gap width is studied based on multiple numerical analysis solutions. The present study deals with two different architectures of a two dimensional model and results have shown that the ventilation velocity has increased to 0.017626444 kg/s and operative air temperature has been decreased by 7.26C for the vertical model while the horizontal model has shown a mass flow rate of 0.018027636 kg/s and a temperature decrease of 9.15C. The most efficient chimney was found to be model 7 which is horizontal solar chimney 3 with an air gap width of 0.05625m and a height of 0.3175 m, when compared to the other models from model number one to six. BEIESP. -
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. -
Prediction of Next-Day Stock Price Using Stacked Ensemble Learning TechniquesAn Exploration of Model Compatibility
Trading professionals can make well-informed decisions about what to purchase or sell in order to maximize short-term gains by forecasting stock prices for the next day. This research study focuses on exploring the compatibility of ensemble learning techniques through stacking to predict next-day stock prices. The models involvedRandom Forest, Extra Trees, AdaBoost, and Gradient Boosting, were paired two at a time, and their predictions were used as inputs to a Multi-Layer Perceptron (MLP) Regressor, which served as the meta-learner. The results revealed that the combination of Extra Trees Regressor and Gradient Boosting outperformed the individual base models, due to their complementary strengths and ability to capture non-linear relationships effectively. However, other model combinations showed only average performance. This outcome was attributed to overlapping model strengths, leading to increase in error and overfitting. The findings highlight the importance of thoughtful model selection in ensemble methods and suggest that not all combinations are equally beneficial. Understanding the compatibility of different models is crucial to improving performance in ensemble learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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 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 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 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).