Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart arrhythmias.The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency.The model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals.The integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia classification.Evaluated on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.

A Review of Machine Learning Techniques in Cyberbullying Detection

Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.

Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

Heart disease is a leading cause ofmortality worldwide.Electrocardiograms(ECG)play a crucial role in diagnosing heart disease.However,interpreting ECGsignals necessitates specialized knowledge and training.The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis.This research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG signals.The proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)networks.By considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over time.The model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart failure.Experimental results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five classes.Furthermore,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease detection.Moreover,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG leads.The proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and diagnosis.This study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease detection.It highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography Images

In the field of stroke imaging, deep learning (DL) has enormousuntapped potential.When clinically significant symptoms of a cerebral strokeare detected, it is crucial to make an urgent diagnosis using available imagingtechniques such as computed tomography (CT) scans. The purpose of thiswork is to classify brain CT images as normal, surviving ischemia or cerebralhemorrhage based on the convolutional neural network (CNN) model. In thisstudy, we propose a computer-aided diagnostic system (CAD) for categorizingcerebral strokes using computed tomography images. Horizontal flip datamagnification techniques were used to obtain more accurate categorization.Image Data Generator to magnify the image in real time and apply anyrandom transformations to each training image. An early stopping method toavoid overtraining. As a result, the proposed methods improved several estimationparameters such as accuracy and recall, compared to other machinelearning methods. A python web application was created to demonstrate theresults of CNN model classification using cloud development techniques. Inour case, the model correctly identified the drawing class as normal with 79%accuracy. Based on the collected results, it was determined that the presentedautomated diagnostic system could be used to assist medical professionals indetecting and classifying brain strokes.

Artificial Intelligence-Enabled Chatbots in Mental Health:A Systematic Review

Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals.Such initiatives,which range from“virtual psychiatrists”to“social robots”in mental health,strive to improve nursing performance and cost management,as well as meeting the mental health needs of vulnerable and underserved populations.Nevertheless,there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings.Furthermore,treatments are frequently developed without clear ethical concerns.While AI-enabled solutions show promise in the realm of mental health,further research is needed to address the ethical and social aspects of these technologies,as well as to establish efficient research and medical practices in this innovative sector.Moreover,the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development.Taking into account all the problems outlined in this study,we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence.In this systematic review,we put five research questions related to technologies in chatbot development,psychological disorders that can be treated by using chatbots,types of therapies that are enabled in chatbots,machine learning models and techniques in chatbot psychologists,as well as ethical challenges.

Artificial Intelligence in Medicine:Real Time Electronic Stethoscope for Heart Diseases Detection

Diseases of the cardiovascular system are one of the major causes of death worldwide.These diseases could be quickly detected by changes in the sound created by the action of the heart.This dynamic auscultations need extensive professional knowledge and emphasis on listening skills.There is also an unmet requirement for a compact cardiac condition early warning device.In this paper,we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods.This system consists of three subsystems that interact with each other(1)a portable digital subsystem of an electronic stethoscope,(2)a decision-making subsystem,and(3)a subsystemfor displaying and visualizing the results in an understandable form.The electronic stethoscope captures the patient’s phonocardiographic sounds,filters and digitizes them,and then sends the resulting phonocardiographic sounds to the decision-making system.The decision-making systemclassifies sounds into normal and abnormal using machine learning techniques,and as a result identifies abnormal heart sounds.The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff,but also for patients and recommends further actions to patients.As a result of the study,we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%.More accurately,the proposed stethoscope can identify normal heart sounds with 93.5%accuracy,abnormal heart sounds with 93.25%accuracy.Moreover,speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

A Skeleton-based Approach for Campus Violence Detection

In this paper,we propose a skeleton-based method to identify violence and aggressive behavior.The approach does not necessitate highprocessing equipment and it can be quickly implemented.Our approach consists of two phases:feature extraction from image sequences to assess a human posture,followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence.A video violence dataset of 400 min comprising a single person’s activities and 20 h of video data including physical violence and aggressive acts,and 13 classifications for distinguishing aggressor and victim behavior were generated.Finally,the proposed method was trained and tested using the collected dataset.The results indicate the accuracy of 97%was achieved in identifying aggressive conduct in video sequences.Furthermore,the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.

Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis.Unfortunately,at the moment,the models for solving this problem using machine learning methods are far from ideal.In this paper,we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3Dcomputed tomography images.We use the ISLES 2018(Ischemic Stroke Lesion Segmentation Challenge 2018)open dataset to train and test the proposed model.Interpretation of the obtained results,as well as the ideas for further experiments are included in the paper.Our evaluation is performed using the Dice or f1 score coefficient and the Jaccard index.Our architecture may simply be extended to ischemia segmentation and computed tomography image identification by selecting relevant hyperparameters.The Dice/f1 score similarity coefficient of our model shown58%and results close to ground truth which is higher than the standard 3D UNet model,demonstrating that our model can accurately segment ischemic stroke.The modified 3D UNet model proposed by us uses an efficient averaging method inside a neural network.Since this set of ISLES is limited in number,using the data augmentation method and neural network regularization methods to prevent overfitting gave the best result.In addition,one of the advantages is the use of the Intersection over Union loss function,which is based on the assessment of the coincidence of the shapes of the recognized zones.

Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

In this research paper,we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus,thereby checking whether it is possible to detect extremist messages in the Kazakh language.To do this,the authors trained models using six classic machine-learning algorithms such as Support Vector Machine,Decision Tree,Random Forest,K Nearest Neighbors,Naive Bayes,and Logistic Regression.To increase the accuracy of detecting extremist texts,we used various characteristics such as Statistical Features,TF-IDF,POS,LIWC,and applied oversampling and undersampling techniques to handle imbalanced data.As a result,we achieved 98%accuracy in detecting religious extremism in Kazakh texts for the collected dataset.Testing the developed machine learningmodels in various databases that are often found in everyday life“Jokes”,“News”,“Toxic content”,“Spam”,“Advertising”has also shown high rates of extremism detection.