Abstract Epilepsy is a widespread neurological disorder characterized by recurring seizures that have significant impact on individuals' lives. Accurately recognizing epileptic crucial for proper diagnosis and treatment. Deep learning models shown promise in improving seizure recognition accuracy. However, optimizing their performance this task remains challenging. This study presents new approach to optimize using deep models. The employed dataset of Electroencephalography (EEG) recordings...

Monkeypox is a rare viral disease that can cause severe illness in humans, presenting with skin lesions and rashes. However, accurately diagnosing monkeypox based on visual inspection of the be challenging time-consuming, especially resource-limited settings where laboratory tests may not available. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown great potential image recognition classification tasks. To this end, study proposes an...

Abstract The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. quality of the selected features plays critical role building effective models, and poor-quality data can make this process more difficult. This work explores use association analysis mining to select meaningful features, addressing issue duplicated information features. A novel technique text proposed, based on frequent correlated items. method considers both...

Abstract This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored two Medical Concept Normalization—Bidirectional Encoder Representations Transformers (MCN-BERT) a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with different hyperparameter optimization method, to predict diseases symptom descriptions. In this paper, utilized distinct dataset called Dataset-1, Dataset-2....

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, public safety. Masked face recognition is essential to accurately identify authenticate individuals wearing masks. has emerged a vital technology address this problem enable accurate identification authentication masked scenarios. In paper, we propose novel method that utilizes combination of deep-learning-based mask...

Abstract Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates accurate prediction models for early diagnosis and effective treatment. Machine learning algorithms have demonstrated promising results in various medical domains, including prediction. In this study, we propose comprehensive approach HCC by comparing the performance different machine before after applying feature reduction methods. We employ popular techniques, such as weighting features,...

Abstract Prediction and classification of diseases are essential in medical science, as it attempts to immune the spread disease discover infected regions from early stages. Machine learning (ML) approaches commonly used for predicting classifying that precisely utilized an efficient tool doctors specialists. This paper proposes a prediction framework based on ML predict Hepatitis C Virus among healthcare workers Egypt. We real-world data National Liver Institute, founded at Menoufiya...

Urinary incontinence (UI) is defined as any uncontrolled urine leakage. Pelvic floor muscles (PFM) appear to be a crucial aspect of trunk and lumbo-pelvic stability, UI one indication pelvic dysfunction. The evaluation tilt lumbar angle critical in assessing the alignment posture spine lower back region pelvis, both these variables are directly related female dysfunction floor. affects significant number women worldwide can have major impact on their quality life. However, traditional...

Blockchain is a revolutionary technology that has the potential to revolutionize various industries, including finance, supply chain management, healthcare, and education. Its decentralized, secure, transparent nature makes it ideal for use in industries where trust, security, efficiency are of paramount importance. The integration blockchain into education system greatly improve efficiency, credibility educational process. By creating secure platforms tracking verifying students' academic...

Abstract Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared monotherapy. However, drug combinations can exhibit synergy, additivity, antagonism. This study presents machine learning framework classify predict combinations. The utilizes several key steps including data collection annotation from the O’Neil interaction dataset, preprocessing, stratified...

Introduction Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer model precise strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, MiT-B5 were thoroughly analyzed enhance detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf...

Abstract The purpose of this study is to investigate the role core muscles in female sexual dysfunction (FSD) and develop comprehensive rehabilitation programs address issue. We aim answer following research questions: what are roles FSD, how can machine deep learning models accurately predict changes during FSD? FSD a common condition that affects women all ages, characterized by symptoms such as decreased libido, difficulty achieving orgasm, pain intercourse. conducted analysis using...

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with emphasis on its implications for policy-making and strategic decisions in energy sector. The model leverages a comprehensive dataset from U.S. Energy Information Administration, spanning over 30 years historical data 1993 to 2023, along relevant temporal features. By combining strengths logic neural networks, ANFIS approach can effectively capture...

This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic considerable interest medical aesthetic fields. We explore potential to alter composition delve into optimization prediction models using advanced hyperparameter techniques, Hyperopt Optuna. Our objective is enhance predictive accuracy dynamics post-cavitation treatment. Employing robust dataset with measurements treatment parameters, we evaluate efficacy our approach...

Summary Feature selection is one of the major issues in pattern recognition. The quality selected features important for classification as low‐quality data can degrade model construction performance. Due to difficulty dealing with problem that always contain redundant information, this article focuses on association analysis theory mining select features. In study, a novel feature method based frequent and associated itemsets (FS‐FAI) text proposed. FS‐FAI seeks find relevant also takes...

Abstract The study proposes a novel model for DNA sequence classification that combines machine learning methods and pattern-matching algorithm. This aims to effectively categorize sequences based on their features enhance the accuracy efficiency of classification. performance proposed is evaluated using various algorithms, results indicate SVM linear classifier achieves highest F1 score among tested algorithms. finding suggests can provide better overall than other algorithms in In...

Abstract With the increasing amount of digital data generated by Arabic speakers, need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing machine learning have shown great promise in field classification. However, there a lack research investigating performance these on language. This paper presents comparative study two datasets language first dataset 213,465 tweets, classic (ML) approaches achieve high...

Abstract Cyberbullying detection systems rely increasingly on machine learning techniques. However, class imbalance in cyberbullying datasets, where the percentage of normal labeled classes is higher than that abnormal ones, presents a significant challenge for classification algorithms. This issue particularly problematic two-class conventional methods tend to perform poorly minority samples due influence majority class. To address this problem, researchers have proposed various...

This study investigates the effectiveness of various deep learning and classical machine techniques in identifying instances cyberbullying. The compares performance five algorithms three models. data undergoes pre-processing, including text cleaning, tokenization, stemming, stop word removal. experiment uses accuracy, precision, recall, F1 score metrics to evaluate on dataset. results show that proposed technique achieves high values, with Focal Loss algorithm achieving highest accuracy 99%...

The global healthcare system faces challenges in diagnosing and managing lung colon cancers, which are significant health burdens. Traditional diagnostic methods inefficient prone to errors, while data privacy security concerns persist.

Abstract This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related and develop accurate models for predicting pain. Machine approaches showed promise analyzing biomechanical factors (LBP). applied regression classification algorithms proposed dataset from 100 postpartum women, 50 LBP without. Optimized optuna Regressor achieved best performance a mean squared error...