LogRF: An Approach to Human Pose Estimation Using Skeleton Landmarks for Physiotherapy Fitness Exercise Correction

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 107930 - 107939

Published: Jan. 1, 2023

Human pose and gesture estimation are crucial in correcting physiotherapy fitness exercises. In recent years, advancements computer vision machine learning approaches have led to the development of sophisticated models that accurately track analyze human movements real time. This technology enables physiotherapists trainers gain valuable insights into their client's exercise forms techniques, facilitating more effective corrections personalized training regimens. research aims propose an efficient artificial intelligence method for during We utilized a multi-class dataset based on skeleton movement points conduct our experimental research. The comprises 133 features derived from various exercises, resulting high feature dimensionality affects performance with deep methods. introduced novel Logistic regression Recursive Feature elimination (LogRF) selection. Extensive experiments demonstrate using top twenty selected features, random forest outperformed state-of-the-art studies high-performance score 0.998. each applied is validated through k-fold approach further enhanced hyperparameter tuning. Our proposed study assists specialists identifying addressing potential biomechanical issues, improper postures, incorrect patterns, which essential injury prevention optimizing outcomes. Furthermore, this enhances capabilities remote monitoring guidance capabilities, allowing support patient's progress prescribed exercises continually.

Language: Английский

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A novel ensemble method for enhancing Internet of Things device security against botnet attacks

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 8, P. 100307 - 100307

Published: Aug. 23, 2023

The growing number of connected Internet Things (IoT) devices has led to the daily growth network botnet attacks. networks compromised controlled by a single entity can be used for malicious purposes such as denial service distributed IoT attacks and theft personal information. weak security measures many make them easy targets compromise inclusion in botnets. In this research, we propose system detecting We develop an ensemble learning detect botnets traffic with high-performance scores. will analyze identify any suspicious behavior that may indicate presence botnet. For purpose, use benchmark CTU-13 dataset build applied machine deep techniques comparison. novel technique, K-neighbors, Decision tree, Random forest (KDR), achieve high performance attack detection. Study results show proposed KDR gives 99.7% accuracy 12.99 s. Hyperparameter optimization k-fold cross-validation are employed substantiate performance. Our research study contributes body knowledge on detection provides practical solution securing against

Language: Английский

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Novel Transfer Learning Based Deep Features for Diagnosis of Down Syndrome in Children Using Facial Images

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 16386 - 16396

Published: Jan. 1, 2024

Down syndrome is a chromosomal condition characterized by the existence of an additional copy chromosome 21. This genetic anomaly leads to range developmental challenges and distinct physical characteristics in affected children. Children with often exhibit specific craniofacial proportions, such as relatively shorter midface broader facial width. These features, including flat nasal bridge, almond-shaped eyes, small somewhat flattened head, can serve valuable indicators for early diagnosis intervention. study aims at using advanced neural network approach. We used 3,009 images children healthy taken from age group 0 15 conducting our research experiments. proposed novel transfer learning-based feature generation named VNL-Net, which ensemble VGG16, Non-Negative Matrix Factorization (NMF), Light Gradient Boosting Machine (LGBM) methods. unique VNL-Net extraction initially extracts spatial features input image data. Then, set NMF LGBM extracted features. built several artificial intelligence-based approaches on newly created evaluate performance. Extensive experimental results show that logistic regression method outperformed state-of-the-art studies high-performance accuracy 0.99. also fine-tuned each applied validated performance k-fold cross-validation mechanism. The runtime computational complexity methods determined. Our innovative has ability revolutionize images.

Language: Английский

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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 9, P. 100352 - 100352

Published: Nov. 4, 2023

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through bites of Anopheles mosquitoes that carry infection. has significant and devastating consequences on health systems fragile countries, particularly in sub-Saharan Africa. affects red blood cells invading replicating within them, destroying releasing toxic byproducts into bloodstream. The parasite's ability stick modify surface can cause them become sticky, obstructing flow vital organs such as brain spleen. Therefore, efficient approaches for early detection malaria are critical saving patients' lives. main aim this study develop an model diagnosis. We used images based parasitized uninfected experiments. applied neural network-based Neural Search Architecture Network (NASNet) compared its performance with machine learning techniques. Moreover, we proposed novel NNR (NASNet Random forest) method feature engineering. approach first extracts spatial features from input images, then class prediction probability extracted these features. set obtained data extraction trains models. Our comprehensive experiments show support vector outperformed state-of-the-art models, achieving high-performance score 99% having inference time near 0.025 s. validated using k-fold cross-validation optimized hyperparameters tuning. research improved diagnosis assist medical specialists reducing mortality rate.

Language: Английский

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Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 103115 - 103131

Published: Jan. 1, 2023

Gunshot sounds are common in crimes, particularly those involving threats, harassment, or killing. The gunshot crimes can create fear and panic among victims, often leading to psychological trauma. associated with a significant mortality rate, especially cases of gun violence. sound gunshots serve as evidence criminal investigations, allowing law enforcement officials determine the number shots fired, caliber used, distance from which were fired. Efficient detection is necessary address issue violence society. This study aims detect using an efficient approach prevent crimes. frequency-time domain spectrum analysis performed understand patterns signals related each target class. A novel Discrete Wavelet Transform Random Forest Probabilistic (DWT-RFP) feature engineering proposed, takes Mel-frequency cepstral coefficients (MFCC) extracted data input for extraction. meta-learning-based Meta-RF-KN (MRK) proposed based on newly created ensemble features DWT-RFP approach. For experiments, dataset containing 851 audio clips collected public videos YouTube eight kinds models, used. Advanced machine learning deep techniques applied comparison evaluate performance Extensive experiments show that MRK achieves 99% k-fold accuracy detecting outperforms state-of-the-art approaches. potentially be used accurate help

Language: Английский

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AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 135507 - 135516

Published: Jan. 1, 2023

Structured Query Language (SQL) injection attacks represent a critical threat to database-driven applications and systems, exploiting vulnerabilities in input fields inject malicious SQL code into database queries. This unauthorized access enables attackers manipulate, retrieve, or even delete sensitive data. The through underscores the importance of robust Artificial Intelligence (AI) based security measures safeguard against attacks. study's primary objective is automated timely detection AI without human intervention. Utilizing preprocessed 46,392 queries, we introduce novel optimized approach, Autoencoder network (AE-Net), for automatic feature engineering. proposed AE-Net extracts new high-level deep features from textual data, subsequently machine learning models performance evaluations. Extensive experimental evaluation reveals that extreme gradient boosting classifier outperforms existing studies with an impressive k-fold accuracy score 0.99 detection. Each applied approach's further enhanced hyperparameter tuning validated via cross-validation. Additionally, statistical t-test analysis assess variations. Our innovative research has potential revolutionize attacks, benefiting specialists organizations.

Language: Английский

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Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits

Diagnostics, Journal Year: 2023, Volume and Issue: 13(18), P. 2949 - 2949

Published: Sept. 14, 2023

Obesity is the excessive accumulation of adipose tissue in body that leads to health risks. The study aimed classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: current employed an observational design, collecting data from public dataset via web-based survey assess eating habits levels. included gender, age, height, weight, family history being overweight, dietary patterns, frequency, more. Data preprocessing involved addressing class imbalance Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) feature selection Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), Extreme Gradient Boosting (XGBoost)) were used for level prediction, Bayesian optimization was hyperparameter tuning. performance different models evaluated metrics such as accuracy, recall, precision, F1-score, area under curve (AUC), precision-recall curve. LR model showed best across most metrics, followed by RF XGBoost. improved models, while XGBoost's mixed. contributes understanding techniques based on demonstrated robust performance, shown enhance efficiency. findings underscore importance both epidemic.

Language: Английский

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Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 138457 - 138471

Published: Jan. 1, 2023

Driver behavior refers to the actions and attitudes of individuals behind wheel a vehicle. Poor driving can have serious consequences, including accidents, injuries, fatalities. One main disadvantages poor is increased risk road higher insurance premiums, fines, even criminal charges. The primary aim our study detect driver early with high-performance scores. publicly available smartphone motion sensor data utilized conduct experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method proposed for feature engineering. combines logistic regression random forest classifier engineering from data. original input into method, generating new probabilistic features. newly extracted features are then applied machine learning methods predicting behavior. results show that approach achieves highest performance score. Extensive experiments demonstrate achieved score 99% using method. validated k-fold cross-validation hyperparameter optimization. Our has potential revolutionize detection avoid accidents.

Language: Английский

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Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 54087 - 54100

Published: Jan. 1, 2024

Depression constitutes a significant mental health condition, impacting an individual's emotional state, thought processes, and ability to carry out everyday tasks. is defined by ongoing feelings of sadness, diminished interest in previously enjoyed activities, alterations hunger, sleep disturbances, decreased vitality, challenges with focus. The impact depression extends beyond the individual, affecting society at large through productivity higher healthcare costs. In realm social media, users often express their thoughts emotions posts, which can provide insightful data for identifying patterns depression. This research aims detect early analyzing media user content machine learning techniques. We have built advanced models using benchmark database containing 20,000 tagged tweets from profiles identified as depressed or non-depressed. are introducing innovative BERT-RF feature engineering method that extracts Contextualized Embeddings Probabilistic Features textual input. Bidirectional Encoder Representations Transformers (BERT) model, based on Transformer architecture, used extract Embedding features. These features then fed into random forest model generate class probabilistic prominent aid enhancing identification media. order classify derived selection step, we five popular classifiers: Random Forest (RF), Multilayer Perceptron (MLP), K-Neighbors Classifier (KNC), Logistic Regression (LR), Long Short-Term Memory (LSTM). Evaluation experiments show our approach, engineering, enables outperform state-of-the-art methods high accuracy score 99%. validated results k-fold cross-validation statistical T-tests. achieved 99% during validation proposed approach. contributes significantly computational linguistics analytics providing robust approach detection content.

Language: Английский

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CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2008 - e2008

Published: May 17, 2024

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These disrupt normal brain functions in various ways, giving rise to broad spectrum of physical, cognitive, emotional challenges. The daily increase mortality rates attributed underscores the urgency this issue. In recent years, advanced imaging techniques, particularly magnetic resonance (MRI), have emerged as indispensable tools diagnosing tumors. MRI scans provide high-resolution, non-invasive visualization structures, facilitating precise detection abnormalities such This study aims propose an neural network approach Our experiments utilized multi-class image dataset comprising 21,672 images related glioma tumors, meningioma pituitary We introduced novel network-based feature engineering approach, combining 2D convolutional (2DCNN) VGG16. resulting 2DCNN-VGG16 (CVG-Net) extracted spatial features from using 2DCNN VGG16 without human intervention. newly created hybrid set is then input into machine learning models diagnose balanced data Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research demonstrate that utilizing proposed CVG-Net, k-neighbors classifier outperformed state-of-the-art studies with k-fold accuracy performance score 0.96. also applied hyperparameter tuning enhance tumor diagnosis. has potential revolutionize early diagnosis, providing professionals cost-effective diagnostic mechanism.

Language: Английский

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