Modelling the minislump spread of superplasticized PPC paste using Random forest, Decision tree and Multiple linear regression

Super plasticizers(SPs) are added to the concrete to improve its workability with out changing the water cement ratio. Property of fresh concrete is mainly governed by the cement paste which depends on the dispersion of cement particle. Cement dispersive properties of the SP depends up on its dosage and the family. Mini slump spread diameter with different dosages and families of SP is taken as the measure of workability characteristic of cement paste chosen for measuring the rheological properties of cement paste. The main purpose of this study includes measure the dispersive ability of different families of SP by conducting minislump test and model the minislump spread diameter of the super plasticized Portland Pozzolona Cement (PPC)paste using regularized least square (RLS) approach along with the application of Random kitchen sink (RKS) algorithm. For preparing test and training data for the model 287 different mixes were prepared in the laboratory at a water cement ratio of 0.37 using four locally available brand of Portland Pozzolona cement (PPC) and SP belonging to four different families. Water content, cement weight and amount of SP (by considering it as seven separate input based on their family and brand) were the input parameters and mini slump spread diameter was the output parameter for the model. The variation of predicted and measured values of spread diameters were compared and validated. From this study it was observed that, the model could effectively predict the minislump spread of cement paste

Engaging in investment activities plays a crucial and strategic role in fostering the growth of businesses and ensuring their resilience in the market. This involvement entails expenditures on acquiring assets, embracing technological advancements, expanding production capacities, conducting research and development, among various other domains. Collectively, these aspects form the foundation for the sustained success of an organization over the long term. This thesis will delve into an exploration of leveraging machine learning techniques to forecast key parameters in business, including investments and their impact on the financial health of the company. In this research, explored a variety of time series models and identified that both the Random Forest Regressor and Decision Tree Regressor models deliver superior accuracy, showcasing identical RMSE values of 88.36 on the validation dataset. Furthermore, the Cat Boost and Light GBM models exhibited praiseworthy performance, registering RMSE values of 92.47 and 104.69, respectively. These findings highlight the robust performance of Random Forest Regressor and Decision Tree Regressor, emphasizing their capability to provide accurate predictions. It is noted that Random Forest Regressor and Decision Tree Regressor are distinguished by high accuracy in time series forecasting, and the choice between them should take into account the trade-offs between computational efficiency and interpretability of the model. These results allow us to propose practical strategies for managing investment resources to ensure the sustainable development and prosperity of the enterprise in the long term.

Stock Classification Prediction Based on Spark

In modern times cardio pulmonary diseases are increasing at an alarming rate. It is a range of serious disorders that affects both the heart (cardio) and the lungs (pulmonary) at times resulting in death. In spite of continuous develop-ment in the field of medical science – both in the surgical and oral drugs, death due to cardiac arrest has remained a major cause of headache for the global population. Research suggests that mortality due to cardiac failure is mainly occurring in the early morning due to high blood pressure fluctuations. An advanced technology like machine learning has shown a new horizon of hope in the medical arena. ML carefully helps to select the patterns best suitable for data sets with various types of algorithms along with different correlation techniques. There are many features that are responsible for the heart disease like age, BP, sodium, cretonne, ejection fraction etc. After identifying important features by applying feature selection and ranking techniques the selected features are used as an input for the different ML algorithms. After imple-menting Random Forest algorithm it was observed that two sets of data tree emerged as comparison of results. Firstly a Decision Tree Classifier was select-ed to control the entire data sets, as well as to check the results with the gen-erated tree for the selected samples. After generating selected samples its re-sults got compared with the classification graph of the decision tree to achieve better result. It was observed that ML algorithms like Random Forest and De-cision Tree Classifier Techniques can helps to predict the major reasons for cardio vascular diseases

Hepatitis C is a viral infectious disease caused that affects 58 million people globally making it a public health crisis, and it is reported that 20-30% of cases develop to severe liver disease. This creates the need for data-driven based method that such as Machine learning that depends on data inputs in making accurate outputs with high precision. This study explores the use of Machine learning algorithms in optimizing the detection of hepatitis C status of patients. This research utilizes a publicly available dataset that contains 12 explanatory features and one target feature that addresses the patient’s diagnosis status. Data preprocessing techniques such as checking of missing values, assessment for possible outliers and Exploratory data analysis was carried on the dataset to improve the quality of the dataset for predictive analysis. The EDA results reviews that majority of the features had significant relationship (p-value < 0.05) with the target variable. Also, we observed the distribution of the disease diagnosis by gender reviews that females showed higher prevalence, and when we consider age, older people are more likely to contract the HCV. This result aligns with research findings from recent studies. The dataset was scaled and separated using a ratio split of 80:20 (80% of data for training). We applied SMOTE algorithm to the training data to balance the diagnosis distribution of the target variable. The model was trained using a wide parameter space using a cross-validation fold of 10 to select the best parameters. The ML models evaluated in this research include Logistic Regression, Support Vector Machine, Random Forest, XGBOOST, Decision Tree, and Extra Trees. The model performance metrics shows that XGBOOST was superior with an accuracy score of 98%, followed by Random Forest (97%), Decision Tree (94%), Extra Trees (93%), SVM (92%), and Logistic Regression (87%). The Kappa statistics for XGBOOST which was estimated as 0.8827 shows that difference between the predicted instances and the actual instances was minimal. The performance metrics of the XGBOOST was also contrast with ML algorithms trained by recent researchers in detecting HCV and it shows our model was more precise underscoring the significance of datapreprocessing, feature-scaling, SMOTE and hyperparameter tuning in predictive analysis in healthcare.

Efficiently predicting the nature of tropical cyclones through machine learning techniques has always posed a challenge in the quest to save human lives. While existing research has proposed various methods to accurately predict cyclone behavior and reduce its impact on humanity, this paper introduces a unique customized Support Vector Machine (SVM) model. Unlike existing models, this machine learning-based custom model enhances evaluation metrics, offering significant improvements in binary classification forecasting. The paper also presents a schematic diagram outlining an architectural design for cyclone nature detection utilizing satellite images. The proposed customized SVM model achieves impressive classification metrics, with accuracy at 95%, precision at 94.78%, recall at 94.5%, and an F1-score of 94.9%. In contrast, other models such as Random Forest (RF), SVM, decision tree (DT), and Logistic Regression (LR) fall short, failing to reach an accuracy exceeding 92%. Furthermore, future work may involve the development of hybrid models.

In today's digital environment, it's reassuring to know that analysis and modelling have gone into improving security in information systems via better trust management of personnel. Security mechanisms like trust are used to deal with varying degrees of authorization within an organization. Building and testing different machine learning models of trust based on the behaviour of an organization's employee data set is the first stage in our trust study. In this work, we show trust modelling on security systems using various machine learning (ML) techniques such as Random Forest (RF), Decision Tree (RF), XG Boost (XGB) and Logistic Regression (LR). We perform the training and the testing of our ML models based on stochastic pattern recognition to classify the Trust of an employee into four classes namely, Trusted, Moderate, U ntrusted and Unexpected. Later a rigorous comparison of all these models is done based on a Model Error Rate (MER) of a recommended trust board.

<p>This study focused on non-technical electricity loss detection. As mentioned, non-technical losses (NTLs) affect utilities and economies financially. Electricity theft, fraud, and metering issues can create NTLs. NTL generate most distribution losses in electrical power networks, costing utilities a lot. NTL detection approaches are data-focused, network-oriented, or hybrid. Data-oriented writing dominated this analysis. After data collection and cleaning and labeling the unlabeled dataset with a target, a methodology was supplied that used four machine learning techniques random forest, decision tree, KNN, and logistic regression and four neural network models-DNN, CNN, CNN-LSTM, and CNN-GRU. The CNN and DNN model have the best accuracy, stability, fast learning, and training time.</p>

Today, everyone is addicted to the internet. Web searching has become important for everyone despite their age, location or occupation. However, as the use of the internet is increasing, the spoofing attacks through malicious websites are also increasing. All conducted online purchases and online pursuits being online amount transaction, e-reservation, recharge online and moreonline. As the number of Internet users grows, automatic URL detection is becoming increasingly more important for protected users. The proposed research will compare the contrasting classification models such as Random Forest, Decision Tree and Logistic Regression which have the highest accuracy of differentiation between legitimate sites from phishing sites. The intention of the research is to find the attacking sites and best ML algorithm which has the highest accuracy.

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p<0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p>0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

Research efforts in the prediction of stroke and diabetes prioritize early detection in order to enhance patient outcomes. To achieve this, a variety of methodologies are integrated. Existing studies, on the other hand, are marred by imbalanced datasets, lack of diversity in their datasets, potential bias, and inadequate model comparisons; these flaws underscore the necessity for more comprehensive and inclusive research methodologies. This paper provides a thorough assessment of machine learning algorithms in the context of early detection and diagnosis of stroke and diabetes. The research employed widely used algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost Classifier, to examine medical data and derive significant findings. The XGBoost Classifier demonstrated superior performance, with an outstanding accuracy, precision, recall, and F1-score of 87.5%. The comparative examination of the algorithms indicated that the Decision Tree, Random Forest, and XGBoost classifiers consistently exhibited strong performance across all measures. The models demonstrated impressive discrimination capabilities, with the XGBoost Classifier and Random Forest reaching accuracy rates of roughly 87.5% and 86.5% respectively. The Decision Tree Classifier exhibited notable performance, with an accuracy rate of 83%. The overall accuracy of the models was evident in the F1-score, a metric that incorporates recall and precision, where the XGBoost model exhibited a marginal improvement of 2% over the Random Forest and Decision Tree models, and 4.25 percent over the last two. The aforementioned results underscore the effectiveness of the XGBoost Classifier, which will be employed as a predictive model in this study, alongside the Random Forest and Decision Tree models, for the accurate identification of stroke and diabetes. Furthermore, combining datasets improves model performance by utilizing relative features. This integrated dataset improves the model's efficiency and creates a resilient and comprehensive prediction model, improving healthcare outcomes. The findings of this research make a valuable contribution to the advancement of AI-driven diagnostic systems, hence enhancing the quality of healthcare decision-making.

Cement pastes undergo elevated temperature histories due to hydration heat liberation at early ages. Thermal expansion coefficients of cement paste and concrete change with age, showing a decrease after mixing, a subsequent minimum and then a gradual increase. These changes contribute to thermal strain. In this study, effects of water–cement ratio and cement type on volume changes in early-age cement pastes were experimentally examined using a newly developed apparatus capable of simultaneously determining both thermal expansion coefficient and total strain of cement pastes. The dependence of the thermal expansion coefficient on hydration was affected by water–cement ratio, cement type, elevated temperature history and particularly by the free water content of the cement pastes, while the relationship between thermal expansion coefficient and free water content varied with water–cement ratio. A notable increase in thermal expansion coefficient at early ages was observed when water–cement ratio was low and alite content in cement was high. At a water–cement ratio of 0.30, low-heat Portland cement paste resulted in a small total strain while moderate-heat and ordinary Portland cement pastes showed larger strains. Because no particular difference was observed in the thermal strains, shrinkage in the low-heat Portland cement paste was attributed to autogenous strain. At a water–cement ratio of 0.40, self-desiccation had a significant influence upon autogenous shrinkage and dependence of thermal expansion coefficient on hydration, and the effect of the mineral composition of cements was notable. However, for cement pastes with a water cement ratio of 0.55, no significant effects of self-desiccation were observed, probably because considerable excess water was present.

Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. The researcher used this algorithm because, in the research that the researcher found on the Kaggle platform, he examined the comparison of the algorithm using the breast cancer dataset. In previous studies, their researchers used SVM, which obtained an accuracy of 96.47%, Neural Networks of 97.06%, and Naïve Bayes with an accuracy of 91.18% to study breast cancer. The difference from previous research is that this study uses several existing algorithms in Machine Learning such as K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. In addition, this research was conducted to see whether the results of the accuracy of the algorithm that the researchers carried out using the lung cancer dataset had different results. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood.

Three machine learning classifiers: random forest, decision tree and support vector machine were used to build predictive models of an anti-mycobacterial ChEMBL database and evaluated for their predictive capability. Before the development of predictive models, data pre-processing was carried out to fix the class imbalance problem by applying cost-sensitive classifier, and filtration of data instance by supervised synthetic minority oversampling technique (SMOTE), spread subsample and resample method. The statistical evaluation indicated that random forest model was the best model as it showed the best accuracy 93.83%, specificity 90.5%, receiver operating characteristic (ROC) 0.984, MCC 0.772 and kappa statistics 0.768 in comparison to other models whereas LibSVM showed the highest sensitivity 94.4% compared with others. Additionally, toxicity predictive models based on SingleCellcall DSSTox carcinogenicity database (AID1189) was developed which resulted in random forest model as the best model. The deployment of both RF predictive models on two unknown datasets resulted in 1317 compounds out of 1554 approved drugs and 2234 compounds out of 18,746 ChEMBL anti-malarial dataset as non-toxic and anti-mycobacterial compounds. Thus machine learning models present highly efficient methods to find out novel hit anti-mycobacterial compounds. We suggest that such machine learning techniques could be very useful to screen drug candidates not only for tuberculosis but also for other diseases.

FakeNews is one of the most popular phenomena that have considerable effects on our social life, especially in the political domain. Nowadays, creating fake news becomes very easy because of users' widespread using the internet and social media. Therefore, the detection of elusiveness news is a crucial problem that needs to be considerable mainly because of its challenges like the limited amount of the benchmark datasets and the amount of the published news every second. This research proposed utilizing two different machine learning algorithms (random forest and decision tree (J48)) to detect the fake news. In this paper, the full dataset size equals 20,761 samples, while the testing sample size equals 4,345 samples.The preprocessing steps start with cleaning data by removing unnecessary special characters, numbers, English letters, and white spaces, and finally, removing stop words is implemented. After that, the most popular feature extraction method (TF-IDF) is used before applying the two suggested classification algorithms. The results show that the best accuracy achieved equals 89.11% using the decision tree model while using the random forest; the accuracy achieved equals 84.97 %.