OPTIMIZING TIME SERIES FORECASTING: LEVERAGING MACHINE LEARNING MODELS FOR ENHANCED PREDICTIVE ACCURACY

Workability is one of the key property of concrete which is governed by water cement ratio. In order to improve the workability of concrete without any variations in water cement ratio Superplasticizers(SPs) are added. Cement paste helps us to analyze the property of fresh concrete where the dispersion of cement particle is taken into account. SP’s Cement dispersive properties are governed by dosage and the family. Various dosages and families of SP are considered for estimating workability feature of cement paste which is picked for investigating on rheological properties through Mini slump spread diameter. The prime motive of this analysis includes measuring the workability of different superplasticizers by conducting a minislump test and hence modelling the flow rate of the superplasticized Portland Pozzolona Cement (PPC)paste using the application of random forest(RF), decision tree(DT) and multiple regression algorithms. Testing and training data for a model were 287 unique mixture compositions at a water by cement ratio was 0.37. This mixture was tested experimentally in a laboratory using four types of locally available PPC’s and of SP which can be broadly categorised in to four families. Amount of seven types of SP brands, water content, cement weight were the input parameters for the model and flow rate was the output parameter. The model’s predicted and experimentally measured values of flow speed were compared and the amount of deviation was recorded.

Stock Classification Prediction Based on Spark

Recent time series forecasting developments indicated that simple linear models can be strong competitors to handle a vast majority of real-world datasets. However, the generality of such models can vary significantly between domains, not to mention between tasks. This paper extends the benchmark of Toner and Darlow Ordinary Least Squares (OLS) was investigated as a competitive baseline model for time series forecasting, to include a few more regression-based models. More specifically, we compare the performances of Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree Regression (DT), and Random Forest Regression (RF) under the same experimental setup and datasets. Our experiments are performed using the ETTh1 benchmark dataset with fixed context horizon and forecasting horizon at 96-time intervals. Compared to the baseline OLS benchmark MSE of 0.376, and the corresponding Mean Absolute Error (MAE) of 0.399, our modified models clearly fare much better. Random Forest Regression does the best, at an MSE of 0.144, an MAE of 0.245, followed closely by Decision Tree Regression (MSE: 0.155, MAE: 0.262) and the KNN (MSE: 0.271, MAE: 0.369). Surprisingly, the modified version of the usual Gaussian Naive Bayes, which has been conditioned to regression using target binning , does an MSE of 1.053, with the corresponding MAE of 0.670, due to the probabilistic nature of the algorithm where the sample domain is not discrete but rather continuous. Our work highlights that Random Forests, which are tree models, are overwhelmingly powerful baselines, surpassing traditional linear methods. This demands a rethinking of the simplicity of definition of forecasting baselines and the use of more varied ML-based regressors as baselines. Source code and experimental configurations are released to facilitate reproduction and further extendibility of the code.

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.

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.

<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.

The growth of every nation's economy depends heavily on the stock market. Because of their strong predictive powers, "K-Nearest Neighbor Regression (KNNR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Support Vector Regression (SVR)" is commonly used. From the previous study, it shows that the chosen state-of-the-art of previous studies is by Shah et al. (2021) using Bi-LSTM. But the state-of-the-art model shows a high Mean Squared Error (MSE) value that is 5% for predict Tesla Stock. Therefore, we propose the SVR, KNNR, DTR, and RFR model hope error value can be reduced. However, those models have difficulties finding suitable parameters for prediction. Due to that, we proposed the best parameters that were commonly used in studies which is SVR with RBF kernel, C = [1; 10; 100], gamma = [0, 1; 0, 2], epsilon = 0,1, KNNR with K = [1, 3, 5, 7, 9], and DTR with a criterion = [‘squared_error’;’friedman_mse’], RFR with 0 < n_estimators < 100 is picked to enhance the predictive capabilities of each proposed model. To prove this, a comparison is made with each proposed model, and choose the best proposed model then compared to the state-of-the-art of the previous study, which is Bi-LSTM. The dataset used was the Tesla Stock historical data from yahoo finance. The result showed RFR with n_estimators = 87 is superior to its comparison with results of 0.005452 RMSE, 0.000029 MSE, and 0.999296 R2 and compared to Bi-LSTM have about 0.11773 RMSE, 0.01386 MSE, and 0.791263 R2. Based on this study, it can be concluded that RFR with n_estimators = 87 has superior predictive capabilities performance compared with other models and the state-of-the-art of previous studies. Doi: 10.28991/HEF-2022-03-04-01 Full Text: PDF

In this study, we investigated the features that could best represent EEG signals for brain computer interface systems and classifier accuracy was compared using different classification methods. EEG signals data set were taken from “BCI II Competition”. In this study, inadequate features that reduce classification accuracy were determined by using sequential forward selection algorithms and were extracted from real-dimensional feature matrix. The remaining active feature matrix and real-dimensional feature matrix were classified using k-nearest neighbor, subspace K-nearest neighbor, support vector machines, subspace discriminant and random forest decision tree algorithms. As a result of this study, the highest classification accuracy of real-dimensional feature matrix was obtained as 83.8% by random forest decision tree algorithm. In the other, the highest classification accuracy of dimention reductioned feature matrix with sequential forward selection algorthm was obtained as 96.4% by random forest decision tree algorithm.

Student outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students’ academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and reduce the failure rate in the future. The main goal of this study is to predict the performance of undergraduate first-level students in the Computer Department during the years 2016 to 2021 to enhance their performance in future by discovering the best algorithm use to analyze the educational data to identify the students’ academic performance. The secondary data was collected by reviewing the Student Affairs Department at the Faculty of Specific Education at Damietta University, in addition to the Statistics Department at the university. The dataset contained 830 instances after excluding 139 instances of missing values, irrelevant rows, and outliers. The dataset was divided into train (577 instances (70%)), test (253 instances (30%)) and involved six features such year, midterm, practical exam, writing exam, final total degree, and grade. This paper use five machine learning (ML) algorithms which was selected according to the literature review and high accuracy in predicting educational data mining: For the purpose of comparison, a number of different machine learning algorithms, such as Random Forest, Decision Tree, Naive Bayes, Neural Network, and K-Nearest Neighbours, were utilized and evaluated with evaluation metrics such as confusion matrix, accuracy, precision, recall, and F-measure. The Random Forest and Decision Tree classifiers emerged as the top-performing algorithms, accurately categorizing 250 instances when predicting students' performance in the statistics course. This was determined based on the findings of the study. Out of a total of 253 instances that were included in the testing set, they only made three incorrect classifications.

Increased levels of oxalic acid are associated with an increased risk of kidney stone formation, which can lead to renal failure. In addition, its high concentration in the blood can lead to cardiovascular diseases. Therefore, it is vital to detect and quantify oxalic acid economically and rapidly. Copper oxide nanoparticles (CuONPs) are gaining importance as colorimetric nanosensors due to their intrinsic color change, cost-effectiveness, and easy synthesis. Paracetamol-mediated CuO NPs were synthesized through a new approach and characterized through various spectroscopic and morphological techniques. UV-visible spectroscopy confirmed the synthesis of CuO NPs through surface plasmon resonance at 225 nm. The peak at 850 cm−1 corresponds to the stretching vibration of CuO NPs. The XRD and SEM characterization techniques confirmed the particle size of 27.51 nm with a spherical morphology. A machine learning-assisted strategy was developed with four prediction models: Random Forest, Linear Regression, XGBoost, and Decision Tree Regression. The intrinsic colorimetric features of CuO NPs were observed through the naked eye and quantified through spectroscopy with the addition of oxalate. The developed platform selectively detected oxalate levels in concentrations ranging from 1 to 120 µM, with a limit of detection (LOD) of 0.23 µM and a limit of quantification (LOQ) of 0.78 µM. The developed biosensor successfully quantified oxalate, crucial for diagnosing hyperoxaluria and preventing calcium oxalate stone formation in the kidneys. The machine learning complementary tools further bolster the accuracy of colorimetric concentration prediction.

Traditional methods of developing new materials, such as observational trial and error and density functional theory (DFT)-based methods, often take enormous time and energy and are constrained by laboratory environments and theoretical foundations. As a result, it is important to formulate a new approach for speeding the process of developing the new materials for the industrial needs. Discovering new material using data analysis techniques has recently received increased interest, with significant increases in prediction accuracy and also time efficiency. So, this paper proposes algorithms for predicting the aluminum composites using various data analysis techniques like linear regression, random forest regressor, K-nearest neighbor (KNN) and decision tree for the required mechanical properties like yield strength, tensile strength, density and thermal conductivity.KeywordsAluminum alloysLinear regressionRandom forest regressorKNNDecision tree

Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in predictive modelling and forensic applications. The current study uses customised machine learning models to predict facial dimensions based on dental and jaw parameters. A sample of 422 participants (201 males and 221 females) from a North Indian population was collected and analysed. Dental casts, anthropometric facial measurements and photographs of the participants were collected with informed consent. ML models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Linear Regression (LR) were trained using dental and jaw measurements as input features for the models. The results show that the ML models predicted the facial dimensions with an accuracy of 90–94% and a very low prediction error of 0.1–0.9 across all facial measurements. Among the models, SVR and LR models perform well, followed by RFR, whereas DFR yielded comparatively lower accuracy. The findings demonstrate that machine learning models (SVR, RFR, DTR, and LR) can be used as novel approach to predict facial dimensions from jaw and teeth parameters. These techniques can be combined with other facial reconstruction techniques to produce more precise and accurate outcomes. The reliability and accuracy in predicting the facial dimensions indicate that the results can be applied in the practical and real situations such as personal identification, forensic investigations, disaster victim identification cases, and archaeological remains where only jaw and teeth are available for examination. Integrating ML-based predictions with traditional facial reconstruction techniques could enhance the accuracy and reliability of forensic identification methodologies.

The rapid spread of contagious diseases poses a colossal threat to human existence. Presently, the emergence of coronavirus COVID-19 which has rightly been declared a global pandemic resulting in so many deaths, confusion as well as huge economic losses is a challenge. It has been suggested by the World Health Organization (WHO) in conjunction with different Government authorities of the world and non-governmental organizations, that efforts to curtail the COVID-19 pandemic should rely principally on measures such as social distancing, identification of infected persons, tracing of possible contacts as well as effective isolation of such person(s) for subsequent medical treatment. The aim of this study is to provide a framework for monitoring Movements of Pandemic Disease Patients and predicting their next geographical locations given the recent trend of infected COVID-19 patients absconding from isolation centres as evidenced in the Nigerian case. The methodology for this study, proposes a system architecture incorporating GPS (Global Positioning System) and Assisted-GPS technologies for monitoring the geographical movements of COVID-19 patients and recording of their movement Trajectory Datasets on the assumption that they are assigned with GPS-enabled devices such as smartphones. Accordingly, fifteen (15) participants (patients) were selected for this study based on the criteria of residency and business activity location. The ensuing participants movements generated 157, 218 Trajectory datasets during a period of 3 weeks. With this dataset, mining of the movement trace, Stay Points (hot spots), relationships, and the prediction of the next probable geographical location of a COVID-19 patient was realized by the application of Artificial Intelligence (AI) and Data Mining techniques such as supervised Machine Learning (ML) algorithms (i.e., Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and eXtreme Gradient Boosting regression(XGBR) as well as density-based clustering methods (i.e., DBSCAN) for the computation of Stay Points (hot spots) of COVID-19 patient. The result of this study showed clearly that it is possible to determine the Stay Points (hot spots) of a COVID-19 patient. In addition, this study demonstrated the possibility of predicting the next probable geographical location of a COVID-19 patient. Correspondingly, Six Machine Learning models (i.e., MLR, kNN, DTR, RFR, GBR, and XGBR) were compared for efficiency, in determining the next probable location of a COVID-19 patient. The result showed that the DTR model performed better compared to other models (i.e., MLR, kNN, RFR, GBR, XGBR) based on four evaluation matrices (i.e., ACCURACY, MAE, MSE, and R2) used. It is recommended that less developed Countries consider adopting this framework as a policy initiative for implementation at this burgeoning phase of COVID-19 infection and beyond. The same applies to the developed Countries. There is indication that GPS Trajectory dataset and Machine Learning algorithms as applied in this paper, appear to possess the potential of performing optimally in a real-life situation of monitoring a COVID-19 patient. This paper is unique given its ability to predict the next probable location of a COVID-19 patient. In the review of extant literature, prediction of the next probable location of a COVID-19 patient was not in evidence using the same Machine Learning algorithms.