Comprehensive Analysis of Twitter Sentiment Using Machine Learning Algorithms for Enhanced Sentiment Prediction

Near infrared spectroscopy was used in discrimination of intact bovine teeth in terms of animal sex, diet, tooth type and place of origin. Discriminant Analysis (DA) models were developed and tested using a stratified random 70:30 data split to calibration and test sets. Of the discriminant techniques of Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares – DA, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), SVM and PLS-DA models performed best in most instances, with pretreatment choice impacting technique success. For test set prediction of animal sex, an accuracy of 95% was achieved using a PLS-DA model, and similar performance achieved using a SVM model. SVM, ANN and SIMCA models predicted three categories of tooth type (deciduous, permanent unerupted and erupted) with similar accuracies (89, 79 and 82%, respectively), however, permanent unerupted teeth were conflated with permanent erupted teeth. Prediction accuracy for ANN, SVM and PLS-DA models were also similar for discrimination of teeth from animals on a grain versus grass diet (PLS-DA 82%). There was considerable variation between models in prediction of geographic origin. Of the seven locations, two were discriminated with true positive rate around 70% using SVM and ANN models. Potential applications of this technology are discussed.

The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.

The Odia language is one of the many regional languages spoken in India. It is the official language of Odisha, a State in eastern India. The Odia language carries a 1500-year-old history and worldwide is spoken by more than 50 million people. The Odia digits are complex due to the presence of many curves in each character. Handwritten scripts are even more complex due to free-style writing. However, the development of an innovative machine learning model is essential because Odia scripts consist of a huge number of historical documents of more than 1000 years old. A robust automation method will help in converting historical documents into digital form and will help to preserve the documents. This will solve a big problem in society. This work experiments with handwritten Odia numerals by implementing two different classifiers. The first one is the implementation of a Convolutional Neural Network (CNN) and the second experiment implements a Support Vector Machine (SVM). Finally, results from both experiments have been compared. The dataset has been generated through software by writing the digits on MS Paint. Both CNN and SVM models have been implemented through Python programming to recognize the inputs into a particular class. Both training and testing of the models have been done using this dataset. The accuracy from the CNN Model is obtained to be 94.999% which is ≈95% and for SVM, the model accuracy is 86%. Comparing both results, it is concluded that the CNN model is comparatively better than the SVM classifier in the case of the proposed work.

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.

This paper compares the performance of five different supervised machine learning (SL) algorithms to identify the practical model for tracking copper using solution cathode glow discharge optical emission spectroscopy (SCGD-OES). A total of 4500 SCGD-OES spectra were collected, corresponding to 15 different copper concentrations ranging from 10.3 mg l−1 to 59.6 mg l−1, for training and validating five models: K-nearest neighbors (KNN), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and recurrent neural network (RNN). Then, performance of these models were evaluated based on accuracy, precision of predictions and training time. In terms of accuracy, the relative error (RE) was 3% for the CNN model and 4% for the ANN model. Conversely, the RE increased from 21% and 43% for the RF and RNN models to 103% for the KNN model due to the unpredictability of Cu concentration outside the training range. Regarding precision, the relative standard deviation (RSD) values were similar across all models, with the lowest RSD being 3% for the CNN model and the highest being 5% for the RF model. The training time indicated that the KNN and RF models provided the best learning speed, while the CNN model had the slowest learning speed, taking 5 times longer than the ANN model. The accuracy of the ANN and CNN models is in agreement with inductively coupled plasma-optical emission spectroscopy (ICP-OES), with a average difference not exceeding 6% when tracking six different Cu concentrations, both inside and outside the training range. Additionally, this research investigated the weight distribution in the hidden layers of the ANN, CNN, and RNN models and feature importances in the RF model to enhance understanding of the internal structure of models and assist analysts in selecting the practical model for tracking heavy metals based on SCGD-OES.

In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%. In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis. The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases. Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.

Image segmentation of brain low-grade glioma (LGG) magnetic resonance imaging (MRI) contributes tremendously to diagnosis, classification and treatment of the disease. A tangible, accurate, reliable and fast image segmentation technique is demanded in clinical diagnosis and research. The emerging machine learning technique has been demonstrated its unique capability in the field of medical image processing, including medical image segmentation. Support vector machine (SVM) and convolutional neural network (CNN) are two widely used machine learning methods. In this work, image segmentation tools based on SVM and CNN are developed and evaluated for brain LGG MR image segmentation studies. The segmentation performance in terms of accuracy and cost is quantitatively analyzed and compared between the SVM and CNN techniques developed. Computed on the Google CoLab, each of the 109 SVM models represents an individual patient, is trained using a single image of that patient and takes a few seconds to complete. The CNN model is trained on a drastically larger dataset of 19,760 data augmented images and takes approximately 2 hours to obtain the most optimal result. The SVM models achieved an average and median accuracy of 0.937 and 0.976 respectively, precision of 0.456 and 0.535 respectively, recall of 0.878 and 0.906 respectively, and F1 score of 0.546 and 0.662 respectively. Although the CNN model required a significantly longer calculation time, it surpassed the SVM models in performance in LGG MR image segmentation, achieving an accuracy of 0.998, a precision of 0.999, a recall of 0.999 and an F1 score of 0.999. This study shows that SVM with appropriate filtering techniques is capable of obtaining reliable and fast segmentation of brain LGG MR images with sufficient accuracy and limited image data. CNN technique outperforms SVM in the accuracy of segmentation with requirements of significantly enlarged data set, long computation time and high-performance computer.

The paper explores the hybrid of SVM and system of Artificial Neural Network as the finest binary classification system for calculating the diabetic nature of people in comparison to Support Vector Machine (SVM). In this research, the sets of all the parameters describing the diabetic nature of a person are taken from the laboratories. This approach is chosen so that a better learning method can be used for various problems. The testing results were found to be in accordance with the accepted results that resemble with the direct diagnosis of a physician. The results of this research shows that this hybrid SVM and Artificial Neural Network (ANN) model is more precise than the SVM model. These results of the hybrid SVM and ANN model suggest that it is very effective for the classification of Diabetic and Non Diabetic nature of a person. This paper highlights the concept of Support Vector Machines and its integration with Artificial Neural Network, the two key characteristics with one being the generalization theory of the SVM model that best describes how to select a hypothesis and functions given by Kernel that introduces the idea of non-linearity without the inclusion of the actual algorithm.

Chatter remains a critical limitation in milling operations, impairing surface integrity, reducing tool life, and limiting productivity. Accurate real-time prediction of chatter is essential for achieving process stability and enhancing machining efficiency. This paper provides a comparative evaluation of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) combined with Variational Mode Decomposition (VMD) for the accurate identification of chatter in real-time vibration data. The milling vibration signals are decomposed into an Intrinsic Mode Functions (IMFs) with VMD to obtain chatter sensitive features. Axial depth of cut, table feed and Spindle speed data combined with these characteristics are used as input to both ANN and SVM models. The ANN model uses Tangent Sigmoid (TANSIG) activation function and six training algorithms, out of which Levenberg-Marquardt (LM) is found to produce the best results. Experimental verification proved that the prediction accuracy of 91.44% was outstripped (over 87.21%) in terms of the SVM model by the ANN model. The robustness of this framework in detecting stable, transitional, and unstable cutting zones has also been demonstrated through Stability Lobe Diagrams (SLDs) analysis. The proposed VMD-ANN approach offers an accurate and efficient solution to chatter prediction and parameter optimisation in real-time, which further contributes to higher material removal rates, better surface quality, and a longer tool life for smart manufacturing applications.

Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

A case study on computer-aided diagnosis of nonerosive reflux disease using deep learning techniques

Dementia is a progressive disease that worsens over time as cognitive abilities deteriorate. Effective preventive interventions require early detection. However, there are no reports in the literature concerning apps that have been developed and designed to predict patient dementia classes (DCs). This study aimed to develop an app that could predict DC automatically and accurately for patients responding to the clinical dementia rating (CDR) instrument. A CDR was applied to 366 outpatients in a hospital in Taiwan, with assessments on 25 and 49 items endorsed by patients and family members, respectively. The 2 models of convolutional neural networks (CNN) and artificial neural networks (ANN) were applied to examine the prediction accuracy based on 5 classes (i.e., no cognitive decline, very mild, mild, moderate, and severe) in 4 scenarios, consisting of 74 (items) in total, 25 in patients, 49 in family, and a combination strategy to select the best in the aforementioned scenarios using the forest plot. Using CDR scores in patients and their families on both axes, patients were dispersed on a radar plot. An app was developed to predict patient DC. We found that ANN had higher accuracy rates than CNN with a ratio of 3:1 in the 4 scenarios. The highest accuracy rate (=93.72%) was shown in the combination scenario of ANN. A significant difference was observed between the CNN and ANN in terms of the accuracy rate. An available ANN-based app for predicting DC in patients was successfully developed and demonstrated in this study. On the basis of a combination strategy and a decision rule, a 74-item ANN model with 285 estimated parameters was developed and included. The development of an app that will assist clinicians in predicting DC in clinical settings is required in the near future.

Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function - firefly algorithm) and SVM-FA (support vector machine - firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R2 value 0.9818 than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.

Convolutional neural network-based wind pressure prediction on low-rise buildings

Understanding the learning mechanism of convolutional neural networks in spectral analysis