Machine Learning Classifiers Research Articles

The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG).

Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information. The present study demonstrates the utility of beat-by-beat features to be useful in classifying IHD subjects (n = 23) and healthy controls (n = 75) in 37-channel MCG data taken under rest condition of subjects. The study reveals the importance of three features (out of eight measured features) namely, the field map angle (FMA) computed from magnetic field map, beat-by-beat variations of alpha angle in the ST-T region and T wave magnitude variations in yielding a better classification accuracy (92.7 %) against that achieved by conventional features (81 %). Further, beat-by-beat features are also found to augment the accuracy in classifying myocardial infarction (MI) Versus control subjects in two public ECG databases (92 % from 88 % and 94 % from 77 %). These demonstrations summarily suggest the importance of beat-by-beat features in clinical diagnosis of ischemia.

Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.

Hazard Identification and Detection Using Machine Learning Approach

Internet surfing has become a vital part of our daily life. So to catch the attention of the users' different browser vendors compete to set up the new functionality and advanced features that become the source of attacks for the intruder and the websites are put at hazard. However, the existing approaches are not adequate to protect the surfers which require an expeditious and precise model that can be able to distinguish between the begnign or malicious webpages. In this research article, we design a new classification system to analyze and detect the malicious web pages using machine learning classifiers such as, random forest, support vector machine. naïve Bayes, logistic regression and Some special URL (Uniform Resource Locator) based on extricated features the classifiers are trained to predict the malicious web pages. The experimental results have shown that the performance of the random forest classifier achieves better accuracy of 95% in comparison to other machine learning classifiers

TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoT

Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT and countering emerging risks require constant updates and monitoring of these devices. Machine learning (ML), in combination with Explainable Artificial Intelligence (XAI), has become an essential component of the CIoT ecosystem due to its rapid advancement and impressive results across several application domains for attack detection, prevention, mitigation, and providing explanations of such decisions. These attacks exploit and steal sensitive data, disrupt the devices’ functionality, or gain unauthorized access to connected networks. This research generates a novel dataset by injecting adversarial attacks into the CICIoT2023 dataset. It presents an adversarial attack detection approach named TXAI-ADV that utilizes deep learning (Mutli-Layer Perceptron (MLP) and Deep Neural Network (DNN)) and machine learning classifiers (K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), ensemble voting, and Meta Classifier) to detect attacks and avert such situations rapidly in a CIoT. This study utilized Shapley Additive Explanations (SHAP) techniques, an XAI technique, to analyze the average impact of each class feature on the proposed models and select optimal features for the adversarial attacks dataset. The results revealed that, with a 96% accuracy rate, the proposed approach effectively detects adversarial attacks in a CIoT.

Enhanced Brain Tumor Classification: A Hybrid Classifier Approach

Early detection of brain tumors is crucial for enhancing patient survival rates and treatment effectiveness. Analyzing magnetic resonance imaging (MRI) images by manually is a difficult task. Therefore, there is a requirement for digital techniques to improve the accuracy of tumor diagnosis. Study of this work was to enhance the performance of Machine Learning classifiers rather than move to deep learning. Machine Learning classifiers are advantageous compared to deep learning methods because of their ability to train on small datasets, low computing time complexity, affordability, and ease of adoption by individuals with less expertise The proposed methodology encompasses image processing, segmentation, feature extraction, feature reduction, and classification, leveraging techniques such as Otsu’s thresholding, GLCM, BWT, and PCA to extract pertinent information while minimizing dimensionality. By integrating a hybrid classifier that combines SVM, DT, and KNN, along with ensemble techniques, significant improvements in classification accuracy and robustness are achieved. Experimental results on the TCGA-GBM dataset showcase accuracy of 96.9%, with high specificity (95.14%) and sensitivity (97.14%) for classify normal tumors (benign tumors) and cancerous tumors (malignant tumors). Furthermore, the proposed methodology demonstrates efficiency with relatively low execution time, rendering it suitable for real-world applications.

Efficient Decoding of Affective States from Video-elicited EEG Signals: An Empirical Investigation

Affect decoding through brain-computer interfacing (BCI) holds great potential to capture users’ feelings and emotional responses via non-invasive electroencephalogram (EEG) sensing. Yet, little research has been conducted to understand efficient decoding when users are exposed to dynamic audiovisual contents. In this regard, we study EEG-based affect decoding from videos in arousal and valence classification tasks, considering the impact of signal length, window size for feature extraction, and frequency bands. We train both classic Machine Learning models (SVMs and k -NNs) and modern Deep Learning models (FCNNs and GTNs). Our results show that: (1) affect can be effectively decoded using less than 1 minute of EEG signal; (2) temporal windows of 6 and 10 seconds provide the best classification performance for classic Machine Learning models but Deep Learning models benefit from much shorter windows of 2 seconds; and (3) any model trained on the Beta band alone achieves similar (sometimes better) performance than when trained on all frequency bands. Taken together, our results indicate that affect decoding can work in more realistic conditions than currently assumed, thus becoming a viable technology for creating better interfaces and user models.

A hybrid quantum ensemble learning model for malicious code detection

Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.

Detecting the symptoms of Parkinson’s disease with non-standard video

BackgroundNeurodegenerative diseases, such as Parkinson’s disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures.MethodsTwenty-eight individuals with Parkinson’s disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person’s body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms.ResultsThe support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively.ConclusionThese results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic.

The potential value of oral microbial signatures for prediction of oral squamous cell carcinoma based on machine learning algorithms.

This study aimed to explore the potential predictive value of oral microbial signatures for oral squamous cell carcinoma (OSCC) risk based on machine learning algorithms. The oral microbiome signatures were assessed in the unstimulated saliva samples of 80 OSCC patients and 179 healthy individuals using 16S rRNA gene sequencing. Four different machine learning classifiers were used to develop prediction models. Compared with control participants, OSCC patients had a higher microbial dysbiosis index (MDI, p < 0.001). Among four machine learning classifiers, random forest (RF) provided the best predictive performance, followed by the support vector machines, artificial neural networks and naive Bayes. After controlling the potential confounders using propensity score matching, the optimal RF model was further developed incorporating a minimal set of 20 bacteria genera, exhibiting better predictive performance than the MDI (AUC: 0.992 vs. 0.775, p < 0.001). The novel MDI and RF model developed in this study based on oral microbiome signatures may serve as noninvasive tools for predicting OSCC risk.

Quantum deep learning-based anomaly detection for enhanced network security

Identifying and mitigating aberrant activities within the network traffic is important to prevent adverse consequences caused by cyber security incidents, which have been increasing significantly in recent times. Existing research mainly focuses on classical machine learning and deep learning-based approaches for detecting such attacks. However, exploiting the power of quantum deep learning to process complex correlation of features for anomaly detection is not well explored. Hence, in this paper, we investigate quantum machine learning and quantum deep learning-based anomaly detection methodologies to accurately detect network attacks. In particular, we propose three novel quantum auto-encoder-based anomaly detection frameworks. Our primary aim is to create hybrid models that leverage the strengths of both quantum and deep learning methodologies for efficient anomaly recognition. The three frameworks are formed by integrating the quantum autoencoder with a quantum one-class support vector machine, a quantum random forest, and a quantum k-nearest neighbor approach. The anomaly detection capability of the frameworks is evaluated using benchmark datasets comprising computer and Internet of Things network flows. Our evaluation demonstrates that all three frameworks have a high potential to detect the network traffic anomalies accurately, while the framework that integrates the quantum autoencoder with the quantum k-nearest neighbor yields the highest accuracy. This demonstrates the promising potential for the development of quantum frameworks for anomaly detection, underscoring their relevance for future advancements in network security.

Assessment of Surgeons’ Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.

Abstract PO4-05-05: Immune Activation Signatures for predicting CDKi primary response in advanced breast cancer patients

Abstract Background Cyclin Dependent Kinase inhibitors (CDKi’s) in combination with Endocrine Therapy (ET) have changed the first line management of patients with the most common subtype (HR+, HER2-) of metastatic breast cancer (MBC), improving progression-free survival (PFS) and overall survival. However, in approved indications for advanced breast cancer, approximately 20% of patients have been observed to have no response to CDKi’s and ET, and to progress after starting these therapies. Unlike other targeted therapeutics, there is no companion diagnostic or other clinically reliable biomarker for clinical decision support to indicate which patients will and will not benefit from CDKi therapy. Methods We have developed a novel platform that measures the Immune Activation Signature of patient plasma samples. Via simple measurements of the total concentration of protein-incorporated amino acid residues within patient plasma samples, the platform is designed to reveal changes in the proportion of immunoglobulins and albumin, and class-switching among immunoglobulins. For that purpose, we use Bioorthogonal chemical labelling reactions to label the protein-incorporated amino acids residues within the plasma. We measured the plasma samples of N=30 HR+, HER2- MBC patients, median age 54 years old, who were baseline naïve for CDKi’s treatment (16 on palbociclib, 11 on ribociclib, and 3 on abemaciclib) and ET. Eighteen patients had metastatic visceral disease and 12 patients had bone-only MBC. All patients were prospectively followed, and response assessed according to RECIST criteria on a CT scan every 3-months. Results Non-responding patients developed objective progressive disease during the first 6 months after starting CDKis and ET. In this cohort, 4 patients (13%) were classified as non-responders. The median PFS in non-responding patients was 4.5 months (4.03-5.02) and the median PFS in the cohort of responding patients was 16.6 months (6.8 – 40.9). The Immune Activation Signatures of the Responding and Non-Responding patients are shown in table 1. We analyzed the results with classical statistics and machine learning. A multivariate analysis of variance (MANOVA) test of the null hypothesis that the Immune Activation Signatures of Non-Responders and Responders are the same gave p = 0.00388. We analyzed the measured Immune Activation Signatures using a supervised machine learning classifier and observed in a held-back validation set correct prediction of 100% of non-responding patients and 95% accurate predictions overall. Conclusions According to these results, this platform may provide a clinically helpful biomarker for clinical decision support in the advanced or aggressive breast cancer context (patients with visceral metastasis bordering on visceral crisis), or for patient stratification in new indications. Table. Immune Activation Signature measurements (mean values in Clinical Outcome) Citation Format: Luis Costa, Cong Tang, Emma Yates, Qi Shi, Wesley Sukdao, Patricia Corredeira, Gonçalo Costa, Helena Pais, Catarina Abreu, Leonor Ribeiro, Rita Teixeira de Sousa, Sofia Torres, André Mansinho, Sandra Casimiro, Ana Cavaco, Patricia Alves, Angela Rodrigues, Lisiana Szeneszi, Gonçalo Bernardes. Immune Activation Signatures for predicting CDKi primary response in advanced breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-05-05.

Abstract PO5-26-05: Machine learning-based risk prediction for late distant recurrence in young women with estrogen receptor-positive/human epidermal growth factor 2-negative breast cancer

Abstract Background There is a clinical unmet need to predict the risk of late recurrence in premenopausal women with estrogen receptor (ER)-positive/human epidermal growth factor 2 (HER2)-negative breast cancer (BC) who completed endocrine treatment (ET). Previous studies suggest that the CTS5 model, developed using data from cohorts with postmenopausal women and predicts a likelihood of late recurrence after five years of ET, is not highly predictive in premenopausal women. We developed a machine learning-based model predicting the chance of late distant recurrence (DR) using multi-institutional cohorts of premenopausal women with ER-positive/HER2-negative BC who have had no DR for five years from the surgery. Methods The study conducted a retrospective review of patients who underwent primary surgery between 2000 and 2011 at Samsung Medical Center, Gangnam Severance Hospital, and Seoul National University Hospital. The study included premenopausal women with ER-positive/HER2-negative BC aged 45 years or younger, who were treated over two years of ET with or without ovarian function suppression and had not experienced distant recurrence for at least five years following the surgery. Patients who had received neoadjuvant chemotherapy were excluded from the study. The primary endpoint is the area under the curve (AUC) and sensitivity (Recall) of the model predicting late DR after five years from primary operation. A total of nine clinical features, including age, tumor size, the number of positive lymph nodes, nuclear grade (NG), histologic grade (HG), progesterone receptor (PR) status, chemotherapy, extension of ET, and addition of OFS, were utilized for supervised machine learning classification. Results A total of 2,555 patients were included in this study. The median age was 41 (range, 21-45) years. During a median follow-up duration of 130.3 (range, 60.0-257.6) months, 157 women (6.1%) had late DR after five years of breast cancer surgery. Age, NG, HG, tumor size, and the number of positive LNs were important clinicopathologic variables for late recurrence. The treatment variables included chemotherapy, extension of ET, and the addition of OFS. There were 1,951 patients (76.4%) who had adjuvant chemotherapy, 650 patients (25.4%) with extension of ET over 5 years, and 529 patients (20.7%) who underwent addition of OFS. To develop the model, we performed repeated 5-fold cross-validation using a Balanced Random Forest classifier. The AUC and sensitivity of the model with six features, including age, tumor size, the number of positive lymph nodes, NG, HG, and PR status, were 0.705 and 0.768, respectively. When chemotherapy was added to this model, the AUC and sensitivity of the model increased to 0.713 and 0.771, respectively. When two more treatment features, such as ET extension and addition of OFS, were added to the model, the AUC and sensitivity were 0.714 and 0.752, respectively. Conclusions Demographic and clinical characteristics of the patients in this study Table. Citation Format: Dong Seung Shin, Janghee Lee, Jong-Ho Cheun, Jun-Hee Lee, Yewon Shin, Soong June Bae, Eunhye Kang, Sunyoung Kwon, Han-Byoel Lee, Jai Min Ryu, Sung Gwe Ahn. Machine learning-based risk prediction for late distant recurrence in young women with estrogen receptor-positive/human epidermal growth factor 2-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-26-05.

Abstract PO2-02-10: Predicting the feasibility of breast conserving surgery using pre-treatment standard of care DCE-MRI: a novel clinical decision support tool for breast cancer surgical planning

Abstract Background: The decision-making process regarding breast cancer treatment options, specifically breast conserving surgery (BCS) versus mastectomy, is a complex and sensitive matter that takes into account the patient's preferences and their eligibility for BCS. A range of anatomical factors, such as tumor size, breast size, and proximity of the tumor to the skin, can influence the surgeon's recommendation regarding BCS or mastectomy. While mastectomy may be the only viable option for patients with large tumors, those with smaller tumors often have the opportunity to collaboratively decide with their surgeon on the most appropriate approach to ensure oncologic success and minimize potential cosmetic deformity. To facilitate this decision-making process, we developed a tool capable of providing informed recommendations on BCS or mastectomy based on standard of care imaging patient data. By integrating these recommendation capabilities, we aim to enhance the decision-making process and improve patient outcomes in breast cancer treatment. Methods: To assist surgeons and patients in determining a patient’s eligibility for breast conserving surgery (BCS), we developed the BCS Feasibility Score. The BCS Feasibility Score is generated from a binary classifier machine learning model and is representative of the probability that a given patient could receive BCS. The score is based solely on the output from TumorSight Viz, a software platform that uses only pre-treatment T1-weighted DCE MRI and applies a suite of deep learning algorithms to segment the patient’s disease and the surrounding breast tissues. From the multi-tissue segmentation generated using TumorSight Viz, we extracted 25 spatial morphology features to use in the model development process. The training and testing sets were a subsample of a publicly available dataset (Saha, et al, 2018). We selected patients that received either BCS or mastectomy and had a viable T1-weighted DCE MRI. Bi-lateral cases were excluded. The total training/testing set consisted of n = 749 cases, 363 of which received mastectomy and 386 who received BCS. Before model training occurred, we split 20% of the sample to use as the test set. We then trained a random forest classifier across a range of pre-set hyper-parameters, using a total of 25 features as inputs, and using 5x cross-validation in the training set. We then assessed model performance in the testing set only. Results: In the test set, 76 cases received BCS versus 74 that received mastectomy. We successfully predicted BCS in 56 out of 76 cases (74%), and mastectomy in 46 out of 74 cases (62%). Overall, we observed an AUC = 0.76 and an F1 score = 0.66, indicating moderate to strong model performance. The most important features in the model, as measured by SHAP values, included the axis aligned tumor longest dimension (mm), the closest distance between the tumor and the nipple (mm), the volume of the tumor convex hull (mL; dilated uni-centrically), and the ratio of tumor convex hull volume to breast volume. We tested the model’s performance in a fully independent holdout validation set of 579 cases. Within this validation set, 335 patients received BCS versus 244 received mastectomy. We successfully predicted BCS in 254 out of 335 cases (76%), and mastectomy in 149 out of 244 cases (61%). Overall, we observed an AUC = 0.75 and an F1 score = 0.63. Validation set performance mirrored results observed in the test set, indicating strong generalizability. In the validation set, comparing patients who underwent BCS to those who underwent mastectomy, we observed significant differences across all four of the most predictive features. Conclusion: TumorSight Viz and the BCS Feasibility Score help empower both patients and clinicians with information and tools to facilitate surgical planning decisions. Citation Format: John Pfeiffer, Bradley Feiger, Anuja Antony, Joseph Peterson. Predicting the feasibility of breast conserving surgery using pre-treatment standard of care DCE-MRI: a novel clinical decision support tool for breast cancer surgical planning [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-10.

Computational kinematics of dance: distinguishing hip hop genres

Dance plays a vital role in human societies across time and culture, with different communities having invented different systems for artistic expression through movement (genres). Differences between genres can be described by experts in words and movements, but these descriptions can only be appreciated by people with certain background abilities. Existing dance notation schemes could be applied to describe genre-differences, however they fall substantially short of being able to capture the important details of movement across a wide spectrum of genres. Our knowledge and practice around dance would benefit from a general, quantitative and human-understandable method of characterizing meaningful differences between aspects of any dance style; a computational kinematics of dance. Here we introduce and apply a novel system for encoding bodily movement as 17 macroscopic, interpretable features, such as expandedness of the body or the frequency of sharp movements. We use this encoding to analyze Hip Hop Dance genres, in part by building a low-cost machine-learning classifier that distinguishes genre with high accuracy. Our study relies on an open dataset (AIST++) of pose-sequences from dancers instructed to perform one of ten Hip Hop genres, such as Breakdance, Popping, or Krump. For comparison we evaluate moderately experienced human observers at discerning these sequence’s genres from movements alone (38% where chance = 10%). The performance of a baseline, Ridge classifier model was fair (48%) and that of the model resulting from our automated machine learning pipeline was strong (76%). This indicates that the selected features represent important dimensions of movement for the expression of the attitudes, stories, and aesthetic values manifested in these dance forms. Our study offers a new window into significant relations of similarity and difference between the genres studied. Given the rich, complex, and culturally shaped nature of these genres, the interpretability of our features, and the lightweight techniques used, our approach has significant potential for generalization to other movement domains and movement-related applications.

Spectral Fusion-based Machine Learning Classifiers for Discriminating Membrane Breakage in Multiple Scenarios

Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV-Vis) spectroscopy to identify membrane breakage. It aims to unravel more comprehensive information, improve detection sensitivity and selectivity, and enable real-time monitoring capabilities. Fluorescence and UV-Vis data are extracted through variance partitioning analysis (VPA) and integrated through a decision tree algorithm to form a superior system with enhanced discrimination capabilities. VPA improves discrimination efficiency by extracting key information from spectral data and eliminating redundancy. The decision tree algorithm, on the other hand, can process large amounts of data simultaneously. In addition, the method has a wide range of applications and can be used in various scenarios accurately. The scenarios include domestic sewage, micropollutant water, aquaculture wastewater, and secondary treated sewage. The experimental results validate the application of machine learning classifiers in membrane breakage detection with an accuracy rate of 96.8% to 97.4%.

The Contribution of Explainable Machine Learning Algorithms Using ROI-based Brain Surface Morphology Parameters in Distinguishing Early-onset Schizophrenia From Bipolar Disorder

Background/aimTo differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry measurements and brain volumes using machine learning (ML) algorithms. MethodHigh-resolution T1-weighted images were obtained to measure cortical thickness (CT), gyrification, gyrification index (GI), sulcal depth (SD), fractal dimension (FD), and brain volumes. After the feature selection step, ML classifiers were applied for each feature set and the combination of them. The SHapley Additive exPlanations (SHAP) technique was implemented to interpret the contribution of each feature. FindingsOne-hundred-forty-four adolescents (16.2±1.4 years, female=40%) with EOS (n=81) and EBD (n=63) were included. The Adaptive Boosting (AdaBoost) algorithm had the highest accuracy (82.75%) in the whole dataset that includes all variables from Destrieux atlas. The best-performing algorithms were K-Nearest Neighbors (KNN) for FD subset, Support Vector Machine (SVM) for SD subset, and AdaBoost for GI subset. The KNN algorithm had the highest accuracy (accuracy=79.31%) in the whole dataset from the Desikan-Killiany-Tourville atlas. ConclusionThis study demonstrates the use of ML in the differential diagnosis of EOS and EBD using surface-based morphometry measurements. Future studies could focus on multicenter data for the validation of these results.

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

In the digital age, social media advertising has become an indispensable tool for marketing. This study investigates the effectiveness of social media advertisements in influencing consumer purchasing decisions. Using a dataset obtained from Kaggle, this analysis incorporates demographic and socio-economic factors—specifically, age, gender, and estimated salary—to predict consumer behavior towards product purchases after exposure to advertisements. Our dataset consists of attributes such as User ID, gender, age, salary, and purchase history. We employed several machine learning classification models including logistic regression, decision trees, random forests, SVM, Naive Bayes, KNN, and Perceptron to identify the most predictive factors and to model purchasing likelihood. Outliers were removed and missing data imputed through statistical methods to ensure data integrity. The highest predictive accuracy was achieved using a Random Forest model with 20 trees, which demonstrated a 95% accuracy rate. This model was integrated into a user-friendly interface developed with Gradio, facilitating real-time predictions. Our findings can assist companies in tailoring their marketing strategies to target demographics most likely to respond positively to advertisements, thereby enhancing the efficiency and effectiveness of their marketing campaigns on social platforms. Keywords:- Social Media Advertising, Consumer Behavior, Predictive Analytics, Machine Learning, Classification Algorithms, Random Forest, Marketing Strategy, Purchase Intentions, Data Preprocessing, Gradio Interface.

Global and local interpretability techniques of supervised machine learning black box models for numerical medical data

The most effective machine learning classification techniques, such as artificial neural networks, are not easily interpretable, which limits their usefulness in critical areas, such as medicine, where errors can have severe consequences. Researchers have been working to balance the trade-off between the model performance and interpretability. In this study, seven interpretability techniques (global surrogate, accumulated local effects, local interpretable model-agnostic explanations (LIME), Shapley additive explanations (SHAP), model agnostic post hoc local explanations (MAPLE), local rule-based explanation (LORE), and Contextual Importance and Utility (CIU)) were evaluated to interpret five medical classifiers (multilayer perceptron, support vector machines, random forests, extreme gradient boosting, and naïve bayes) using six model performance metrics and three interpretability technique metrics across six medical numerical datasets. The results confirmed the effectiveness of integrating global and local interpretability techniques, and highlighted the superior performance of global SHAP explainer and local CIU explanations. The quantitative evaluations of explanations emphasised the importance of assessing these interpretability techniques before employing them to interpret black box models.

Artificial Intelligence in Ship Trajectory Prediction

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research.