University Of California , Irvine Research Articles

Hybrid deep learning model for YouTube spam comment detection

Social media platforms, including YouTube and Facebook, allow users to interact through comments and videos. However, the openness of these platforms also makes them susceptible to spammers engaging in phishing, malware distribution, and advertisement dissemination. In response, our study introduces an innovative technique for detecting features indicative of spam within comments associated with shared videos. The initial phase involves data collection from the University of California, Irvine (UCI) machine learning repository and preprocessing using tokenization and lemmatization. Subsequently, a rigorous feature selection process is executed, and experiments are conducted with various proposed classification models. The performance evaluation demonstrates outstanding accuracy in identifying spam comments on YouTube: convolutional neural network with gated recurrent unit (CNN-GRU) at 95.92%, convolutional neural network with long short-term memory (CNN-LSTM) at 95.41%, convolutional neural network with bidirectional long short-term memory (CNN-biLSTM) at 96.43%, gated recurrent unit (GRU) at 95.41%, long short-term memory (LSTM) at 94.13%, and bidirectional long short-term memory (biLSTM) at 96.94% and convolutional neural network (CNN) at 94.64%. These results highlight the substantial contribution of our approach to spam detection and the fortification of online security.

Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques

Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Vector Classifier, Decision Tree, Random Forest, and Gradient Boosting, by merging their predictions and utilizing them as input features for a meta-classifier during training. We tested and validated the ensemble model using the dataset from the University of California, Irvine (UCI) repository to assess its effectiveness. The Individual classifiers achieved different accuracies: Naïve Bayes (85.41%), Support Vector Machine (98.61%), Random Forest (97.91%), Decision Tree (95.13%), Gradient Boosting (95.83%). The stacking method yielded a higher accuracy of 99.30% and a precision of 1.00, recall of 0.96, F1 score of 0.97, and specificity of 1.00 compared to the base models. The study confirms the effectiveness of ensemble learning techniques in classifying ESD.

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals

The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, no diagnostic procedures exist for this disease. However, several computational approaches have proven successful in detecting PD at early stages, overcoming the disadvantages of traditional methods of diagnosis. In this study, a machine learning (ML) detection system based on the voice signals of PD patients is proposed. The AdaBoost classifier has been utilized to construct the model and trained on a dataset obtained from the machine learning repository of the University of California, Irvine (UCI). This dataset includes voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrated promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Furthermore, the robustness of the proposed model is rigorously assessed through cross-validation, revealing consistent performance across all iterations. The overarching objective of this study is to contribute to the scientific community by furnishing a robust system for the detection of PD.

A Fast Feedforward Small-World Neural Network for Nonlinear System Modeling.

It is well-documented that cross-layer connections in feedforward small-world neural networks (FSWNNs) enhance the efficient transmission for gradients, thus improving its generalization ability with a fast learning. However, the merits of long-distance cross-layer connections are not fully utilized due to the random rewiring. In this study, aiming to further improve the learning efficiency, a fast FSWNN (FFSWNN) is proposed by taking into account the positive effects of long-distance cross-layer connections, and applied to nonlinear system modeling. First, a novel rewiring rule by giving priority to long-distance cross-layer connections is proposed to increase the gradient transmission efficiency when constructing FFSWNN. Second, an improved ridge regression method is put forward to determine the initial weights with high activation for the sigmoidal neurons in FFSWNN. Finally, to further improve the learning efficiency, an asynchronous learning algorithm is designed to train FFSWNN, with the weights connected to the output layer updated by the ridge regression method and other weights by the gradient descent method. Several experiments are conducted on four benchmark datasets from the University of California Irvine (UCI) machine learning repository and two datasets from real-life problems to evaluate the performance of FFSWNN on nonlinear system modeling. The results show that FFSWNN has significantly faster convergence speed and higher modeling accuracy than the comparative models, and the positive effects of the novel rewiring rule, the improved weight initialization, and the asynchronous learning algorithm on learning efficiency are demonstrated.

Exploring Pediatric Vision Care: Insights from Five Years of Referral Cases in the UCI Eye Mobile and Implications of COVID-19.

To analyze referral rates, patient demographics, referral indications, and the impact of socioeconomic factors on ocular health from the University of California Irvine (UCI) Eye Mobile for Children, particularly during the coronavirus disease 2019 (COVID-19) pandemic. A retrospective chart review was performed on de-identified records of children examined on the UCI Eye Mobile. GraphPad Prism 10.0.0 and Python software were used for statistical analyses. In the academic years from 2018 to 2022, 3,619 children received comprehensive eye examinations on the UCI Eye Mobile. Among them, 76 were referred to a pediatric ophthalmologist. The majority of these children were Hispanic (72.6%, 54 of 74), followed by Asian (10.9%, 8 of 74). A significant proportion (82.9%, 63 of 76) attended school districts with median incomes below that of Orange County. Statistically significant differences were found in age (P = .001; pre-COVID: 3.98 ± 1.08 years vs COVID: 5.75 ± 2.92 years) and gender (P = .023; pre-COVID female: 31 of 41 vs COVID female: 15 of 32) between the pre-COVID and COVID years. Additionally, there were significant differences in the proportion of children with hyperopia with astigmatism between the pre-COVID and COVID years (P = .044; pre-COVID: 23 of 40 vs COVID: 12 of 35). The most common indications for ophthalmologist referrals were for strabismus evaluation/treatment (28.9%, 22 of 76), followed by abnormal cup-to-disc ratio (21.1%, 16 of 76). The study highlights the pivotal role of the UCI Eye Mobile for children in identifying ocular conditions needing referrals to subspecialty care. The majority of children needing these referrals attended schools in lower economic communities. Additionally, the COVID-19 pandemic appears to have influenced the demographic and clinical characteristics. [J Pediatr Ophthalmol Strabismus. 20XX:X(X):XXX-XXX.].

SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES

SMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage. Through marketing campaigns and attempts to gain private information, such as credit card numbers, these spam messages seek to further business or financial objectives. The duty of removing spam mails has therefore grown in significance. In response, a number of deep learning and machine learning methods have been used to identify SMS spam. Using data from the University of California, Irvine (UCI), this research examines the application of such strategies. Key Words: MultinomailNaiveBayes, SMS Spam Detection, NLTK, Vectorization, Feature Extraction.

Human Hand Movement Classification based on EMG Signal using different Feature Extractor

Electromyography (EMG) based hand movement classification plays a significant role in various fields, namely in prosthetics, rehabilitation, biomechanics, etc. This paper presents the study of EMG-based hand movement classification of 3 human hand gestures (hand at rest, wrist flexion, and wrist extension). The dataset was officially collected from the University of California, Irvine (UCI) machine learning repository. The dataset contains 8 channels and 3 classes representing 3 human hand gestures, with 15000 rows of EMG data for each class. The dataset obtained was raw and unprocessed, to filter this dataset Notch and Butterworth filters were used. After filtering, the sliding window was performed. Various feature extraction techniques, namely frequency domain features (FD) and discrete wavelet transform (DWT) were applied separately on the window dataset and then accuracy was tested on different classifiers, namely random forest (RF), k- nearest neighbor (KNN), and decision tree (DT). As a novel approach, time domain (TD) and DWT extracted features were fused together and then given to the classifiers to test accuracy. Among all these feature extractors, the features extracted by FD provided the highest accuracy of 81.69 for the RF classifier.

Implementing a cancer survivorship seminar course to non-healthcare professional undergraduate students

BackgroundAt University of California, Irvine (UCI), a seminar course focused on cancer survivorship was developed and offered to non-healthcare professional undergraduate students. Utilizing the knowledge, attitude, and perception (KAP) framework, this study was designed to examine the impact on students who have taken this course, and to clarify the value of this course for undergraduate students.MethodsThis was a cross-sectional survey. Undergraduate students enrolled in the Life After Cancer Freshmen Seminar course (Uni Stu 3) at UCI between 2021 and 2023 were invited to participate. The survey consisted of 4 main sections: (1) demographics, (2) knowledge of cancer survivorship, (3) attitude towards cancer survivorship, and (4) perception and awareness of cancer survivorship. The survey was administered prior to the implementation of the course, and the same survey was administered at the end of the course.ResultsA total of 33 students completed the pre-implementation survey and 30 students completed the post-implementation survey. Comparing pre- and post-course implementation, there was an increase of perception and awareness of (i) resources and guidelines for cancer survivors (pre, 9.1% vs. post, 36.7%), (ii) mental health complications among cancer survivors (pre, 36.4% vs. post, 56.7%), (iii) benefits of cancer survivorship care (pre, 15.2% vs. post, 40%), latest research in cancer survivorship (pre, 0% vs. post, 23.3%), and (iv) tailoring survivors’ needs according to their age groups (pre, 24.2% vs. post, 66.7%). Knowledge and attitude towards caring of cancer survivors were similar comparing pre- and post-course implementation.ConclusionIn an undergraduate seminar course focused on cancer survivorship, we observed an improvement of non-healthcare students’ perception and awareness of cancer survivorship-related issues, advocating the value on introducing highly prevalent cancer survivorship topics early to both undergraduate STEM and non-STEM students.

Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection

Breast cancer has been one of the leading causes of death among women in the world. Early detection of this disease can save patient’s lives and reduce mortality. Due to the large number of features involved in the diagnosis of this disease, the breast cancer diagnosis process can be time consuming. To reduce cost and time and improving accuracy of breast cancer diagnosis, this paper propose a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods for selection the most effective features for breast cancer diagnosis among all features. In order to evaluate the efficiency of the proposed feature selection method, it was tested on three most common breast cancer datasets available in the University of California, Irvine (UCI) repository named: Coimbra dataset (CD), Wisconsin Diagnostic Breast Cancer dataset (WDBC) and Wisconsin Prognostic Breast Cancer dataset (WPBC). In the Coimbra dataset with all its 9 features and without PSO feature selection algorithm the highest obtained accuracy was 87% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 91% and the number of features was reduced from 9 to 4. In the WDBC dataset with all its 30 features and without PSO feature selection algorithm the highest obtained accuracy was 99% by Random Forest method, while with PSO feature selection algorithm the accuracy reached to 100% and the number of features was reduced from 30 to 19. In the WPBC dataset with all its 33 features and without PSO feature selection algorithm the highest obtained accuracy was 94% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 96% and the number of features was reduced from 33 to 17. The results of this paper indicated that the proposed feature selection algorithm based on PSO algorithm can improve the accuracy of breast cancer diagnosis. While it has selected fewer and more effective features than the total number of features in the original datasets.

Differential diagnosis of erythemato-squamous diseases using a hybrid ensemble machine learning technique

Erythemato-squamous Diseases (ESD) encompass a group of common skin conditions, including psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris. These dermatological conditions affect a significant portion of the population and present a current challenge for accurate diagnosis and classification. Traditional classification methods struggle due to shared characteristics among these diseases. Machine Learning offers a valuable tool for aiding clinical decision-making in ESD classification. In this study, we leverage the UC Irvine (UCI) dermatology dataset by applying necessary preprocessing steps to handle missing data. We conduct a comparative analysis of two feature selection methods: One-way ANOVA and Chi-square test. To enhance the model’s performance, we employ hyper-parameter tuning through GridSearchCV. The training process encompasses various algorithms, including Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbors (kNN), and Decision Trees. The culmination of our work is a hybrid ensemble machine learning model that combines the strengths of the trained classifiers. This ensemble classifier achieves an impressive accuracy of 98.9% when validated using a 10-fold cross-validation approach.

Machine learning algorithms-based decision support model for diabetes

This research explores the application of machine learning (ML)-based risk prediction models in early diabetes disease detection for healthcare professionals. Diabetes affects millions of people worldwide. In light of significant advancements in biomedical sciences, vast volumes of data have been generated, including high-throughput genetic and diagnostic data sourced from extensive health records. Leveraging an initial diabetes risk prediction dataset from the University of California Irvine (UCI) ML repository, our research focused on supervised learning techniques, constituting 85% of the employed methods. The remaining 15% comprised unsupervised learning approaches, specifically association rules. A key contribution of this study lies in the development of an optimal prediction model utilizing supervised ML algorithms. The Boruta feature selection algorithm was employed to identify pertinent features, and the subsequent models were validated using a preprocessed dataset containing 10 attributes. Notably, the risk prediction models generated through random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) exhibited impressive average accuracies of 98.13%, 97.37%, and 97.22%, respectively, as determined via 10-fold cross-validation with 15 repetitions. Furthermore, these models achieved exceptional area under the ROC curve (AUC) values of 1, 0.99, and 0.99, respectively, showcasing their robustness and efficacy in diabetes risk prediction.

Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm.

Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (dc). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of dc when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize dc for maximizing accuracy, achieving automatic determination of dc. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC.

A cloud model-based interval-valued evidence fusion method and its application in fault diagnosis

In fault diagnosis methods based on evidence fusion, a piece of evidence is usually extracted from the fault feature information, which uses the single-valued belief degrees (SVB) to measure the likelihoods of the different fault modes happening. However, SVB seems rough and incomplete for modeling fault information with uncertainty. Therefore, an interval-valued evidence (IVE) fusion method is proposed via cloud model. Compared to the SVB structure, the IVE with the interval-valued belief structure contains more useful fault information. Firstly, the nominal normal cloud model (NCM) about each fault mode and the test normal cloud model (TNCM) are established through the statistical analysis of the historical and testing fault sample data, respectively. TNCMs are matched with each NCM to obtain the corresponding IVE. Then, a novel IVE reasoning rule is defined to combine all IVEs coming from different fault features. Finally, in the fault diagnosis experiments of motor rotors, the proposed method is verified to provide more accurate fault decision results compared to the traditional evidence fusion method. Furthermore, the proposed method is extended to solve general data classification problems with benchmark datasets of University of California Irvine (UCI), showing it has better classification ability compared to some classification methods.

Enhancing data security of cardiac patients in IoMT with Twin-Shield Encryption

<p>Cardiac disease kills most people worldwide. Predicting and monitoring cardiac problems early improves disease treatment and patient outcomes. The Internet of Medical Things (IoMT) can monitor and analyze physiological data in real-time, changing healthcare. Many researchers find data generation problematic. Encryption is needed to secure a massive amount of data. This paper presents a Twin-Shield Encryption (TSE) that combines Elliptic Curve Cryptography (HECC) and Rivest-Shamir-Adleman (RSA) IoMT assistance for heart illness patient monitoring. Cleveland cardiac dataset from the University of California Irvine (UCI) research repository is collected. It has 12 qualities and 303 occurrences. The data is pre-processed using normalization; feature extracted using Principal Component Analysis (PCA), and securely transmitted to the cloud infrastructure for further processing and analysis. TSE encrypts patient data to prevent unauthorized access and maintain data integrity during transmission and storage. The framework could enhance cardiac ailment diagnosis, treatment, and management by giving clinicians and patients individualized care based on physiological profiles.</p>

Robust attribute selection to improve the Parkinson's disease classification: a hybrid approach

Parkinson’s disease (PD) is known to be a neurodegenerative syndrome that progresses chronically. As a result of the damage or death of brain neurons that generate dopamine patients tend to face difficulty when performing simple everyday tasks like walking, writing, or speaking. The main contribution of this work presents a hybrid method for improving predicting PD. This methodology has been obtained by means of testing a number of different combinations of classification algorithms and approaches for selecting attributes. A total of three attributes selection methods (correlation, information gain, and variance threshold) and three classifiers (decision trees (DT), naive bayes (NB), and support vector machine (SVM)) have been adopted. The speech data set provided by University of California-Irvine (UCI) machine learning (ML) repository is adopted to analyze the performance of different combinations. The combination of information gain and DT classifier achieved the best performance rather than other combination methods, reaching a classification accuracy of (97.43%). Finally, an additional comparison of the performance analysis with the results of previous studies was made and it was found that the proposed methodology proved to outperform the results of other studies conducted in this field.

An ensemble learning-based framework for breast cancer prediction

Breast cancer is a disease characterised by abnormal cell growth in the breast, with various types and unique characteristics depending on the malignant cells involved. It predominantly affects women, and researchers in the field of clinical sciences are increasingly interested in leveraging Artificial Intelligence (AI) techniques to predict and diagnose breast cancer. This study utilises the widely-used Wisconsin Breast Cancer Dataset (WBCD) from the University of California Irvine (UCI) machine learning repository. The dataset contains 30 features, including mean, standard error, and “worst” values, providing insights into different aspects of breast cancer. A stacked ensemble classifier is proposed to evaluate the effectiveness of the proposed model, incorporating multiple machine learning algorithms such as the Decision Tree Classifier, AdaBoost Classifier, Gaussian Naive Bayes (GaussianNB), and Multi-layer Perceptron Classifier (MLPClassifier). Various performance measures, including the Receiver Operating Characteristic Curve (ROC curve), Area Under the Curve (AUC), specificity, F1-score, sensitivity, and accuracy, are used to assess the model’s performance. The results demonstrate that the proposed ensemble technique achieves an accuracy of 97.66%, surpassing the performance reported in existing literature. This highlights the superior performance of the developed model compared to previous approaches. The study underscores the potential of AI techniques and machine learning algorithms in advancing breast cancer prediction and diagnosis, offering promising avenues for improving clinical decision-making and patient outcomes.

Remote sensing-based scene classification by feature fusion and extraction with ensemble classifier employing machine learning approaches

This research focuses on scene segmentation in remotely sensed images within the field of Remote Sensing Image Scene Understanding (RSISU). Leveraging recent advancements in Deep Learning (DL), particularly Residual Neural Networks (RESNET-50 and RESNET-101), and the research proposes a methodology involving feature fusing, extraction, and classification for categorizing remote sensing images. The approach employs a dataset from the University of California Irvine (UCI) comprising twenty-one groups of pictures. The images undergo pre-processing, feature extraction using the mentioned DL frameworks, and subsequent categorization through an ensemble classification structure combining Kernel Extreme Learning Machine (KELM) and Support Vector Machine (SVM). The paper concludes with optimal results achieved through performance and comparison analyses.

A hybrid wrapper spider monkey optimization-simulated annealing model for optimal feature selection

In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration and exploitation, a hybrid model with a popular meta-heuristic algorithm named spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset from dataset by removing the noise and outliers prior to feeding the data to the wrapper SMO. To enhance the quality of the solution, simulated annealing is deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.

Custom machine learning algorithm for large-scale disease screening - taking heart disease data as an example

Heart disease accounts for millions of deaths worldwide annually, representing a major public health concern. Large-scale heart disease screening can yield significant benefits both in terms of lives saved and economic costs. In this study, we introduce a novel algorithm that trains a patient-specific machine learning model, aligning with the real-world demands of extensive disease screening. Customization is achieved by concentrating on three key aspects: data processing, neural network architecture, and loss function formulation. Our approach integrates individual patient data to bolster model accuracy, ensuring dependable disease detection.We assessed our models using two prominent heart disease datasets: the Cleveland dataset and the UC Irvine (UCI) combination dataset. Our models showcased notable results, achieving accuracy and recall rates beyond 95 % for the Cleveland dataset and surpassing 97 % accuracy for the UCI dataset. Moreover, in terms of medical ethics and operability, our approach outperformed traditional, general-purpose machine learning algorithms. Our algorithm provides a powerful tool for large-scale disease screening and has the potential to save lives and reduce the economic burden of heart disease.