Advanced Feature Extraction Techniques Research Articles

Deep Learning-Based Detection of Abdominal Diseases Using YOLOv9 Models and Advanced Preprocessing Techniques

Artificial intelligence has emerged as a transformative tool in medical imaging, enabling automated diagnosis and analysis across various domains. While significant advancements have been made in abdominal imaging, many studies struggle to achieve robust detection of diseases. The complexity and variability in abdominal structures present unique challenges for traditional machine learning models, necessitating the adoption of more advanced object detection frameworks. Motivated by these challenges, this study focuses on leveraging the YOLOv9 object detection architecture to enhance the identification of abdominal diseases using the TEKNOFEST 2022 Abdomen Dataset. Advanced preprocessing techniques, including CLAHE (Contrast Limited Adaptive Histogram Equalization) and Gaussian noise augmentation, were applied to improve image contrast and model robustness. The dataset was processed into YOLO-compatible formats, and multiple training configurations were evaluated using YOLOv9c and YOLOv9s variants. These configurations included variations in batch size, optimizer type (SGD and Adam), dropout rate, and frozen layers. Among the configurations tested, the YOLOv9s model with 32 batch size, SGD optimizer, and a 35% dropout rate demonstrated the best performance, achieving a Recall of 0.7698, Accuracy of 0.7698, and F1 Score of 0.8228. The highest mAP50 of 0.9385 was observed with the YOLOv9c model trained using the Adam optimizer and a 35% dropout rate. Confusion matrix analysis revealed strong detection capabilities for conditions like acute cholecystitis and abdominal aortic aneurysm. This study highlights the potential of YOLOv9 models in medical imaging and emphasizes the importance of high-resolution datasets and advanced feature extraction techniques for improving diagnostic accuracy in abdominal disease detection. These findings lay a foundation for the development of reliable and efficient AI-driven diagnostic tools.

Semantic Object Segmentation using Modified HRNet Deep Learning Model

This paper presents a modified High-Resolution Network (HRNet) architecture designed for enhanced semantic object segmentation, addressing the prevalent challenges of accuracy and computational efficiency in complex image analyses. Through strategic modifications to the conventional HRNet, including optimized feature blocks and advanced feature extraction techniques, our model demonstrates significant improvements in segmentation performance. Utilizing the Cityscapes dataset, renowned for its comprehensive urban scene representation, our enhanced HRNet model achieved a notable increase in validation accuracy to 85.8% and mean Intersection over Union (mIoU) to 63.43%, surpassing the original HRNet benchmarks by 3.39% and 3.43%, respectively. These results highlight not only the precision of our model in delineating intricate urban landscapes but also its robustness in handling diverse object scales and complexities. The qualitative analysis, underscored by comparison images, reveals our model's ability to produce more defined segmentation contours and accurate object identifications, setting a new standard for HRNet-based semantic segmentation. This work not only advances the field of high-resolution image segmentation but also offers a foundational model for future research aimed at solving increasingly complex image processing challenges

Optimized Music Classification with a Hybrid VGG16-RNN Using Mel-Spectrogram and MFCC Features

Music classification using deep neural networks has gained a lot of attention in recent years. This is due to the difficult task of capturing every essential aspect of music in features and interpretability of classifiers. There is limited research on the integration of VGG16 and RNNs, but the researchers found that few classifiers accurately capture intrinsic musical characteristics. Previous work in this field has primarily focused on spectral features, which has constrained overall performance. To address this issue, we proposed a novel hybrid neural architecture based on Visual Geometry Group 16 (VGG16), which is highly effective in extracting important features from musical variations. We combined VGG16 with several recurrent neural network (RNN) variants, including Gated Recurrent Unit (GRU), Bidirectional GRU (BiGRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Additionally, we compared their performance for the GTZAN dataset using both Mel-Spectrogram and Mel-Frequency Cepstral Coefficients (MFCC) features. Our results indicate that the VGG16+GRU model achieved the highest accuracy of 89. 60% with Mel spectrograms and 82. 70% with MFCC features. These findings demonstrate the effectiveness of combining advanced feature extraction techniques with deep learning models for music genre classification.

Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review

Crop diseases pose a significant threat to global food security, with both economic and environmental consequences. Early and accurate detection is essential for timely intervention and sustainable farming. This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. The review method includes an in-depth analysis of algorithm performance using key metrics such as accuracy, precision, recall, and F1 score across various datasets. We also highlight the data imbalances in commonly used datasets, particularly PlantVillage, and discuss the challenges posed by these imbalances. The research highlights critical insights regarding ML and DL models in crop disease detection. A primary challenge identified is the imbalance in the PlantVillage dataset, with a high number of healthy images and a strong bias toward certain disease categories like fungi, leaving other categories like mites and molds underrepresented. This imbalance complicates model generalization, indicating a need for preprocessing steps to enhance performance. This study also shows that combining Vision Transformers (ViTs) with Green Chromatic Coordinates and hybridizing these with SVM achieves high classification accuracy, emphasizing the value of advanced feature extraction techniques in improving model efficacy. In terms of comparative performance, DL architectures like ResNet50, VGG16, and convolutional neural network demonstrated robust accuracy (95–99%) across diverse datasets, underscoring their effectiveness in managing complex image data. Additionally, traditional ML models exhibited varied strengths; for instance, SVM performed better on balanced datasets, while RF excelled with imbalanced data. Preprocessing methods like K-means clustering, Fuzzy C-Means, and PCA, along with ensemble approaches, further improved model accuracy. Lastly, the study underscores that high-quality, well-labeled datasets, stakeholder involvement, and comprehensive evaluation metrics such as F1 score and precision are crucial for optimizing ML and DL models, making them more effective for real-world applications in sustainable agriculture.

Enhancing Email Security: A Real‐Time Machine Learning‐Based Spam Detection System

ABSTRACTSpam emails pose a persistent threat to online security and productivity. To combat this menace, we propose a robust machine learning‐based spam email detection system, leveraging algorithms like Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF). Our system integrates Python's Natural Language Processing (NLP) library and cloud sourcing to achieve unparalleled accuracy and adaptability. Advanced feature extraction techniques, including Term Frequency‐Inverse Document Frequency (TF‐IDF) and N‐grams, enable comprehensive analysis of email content. Real‐time scanning and detection capabilities ensure immediate protection, while continuous learning allows the system to evolve and counter emerging spam tactics. Our system demonstrates exceptional performance, achieving accuracy rates of up to 97.58% with SVM. Comparative analysis of 11 machine learning algorithms reveals notable performance variations. By minimizing false positives and negatives, our system provides reliable protection against unwanted and harmful emails. This research contributes to the development of effective spam detection solutions, offering insights into optimal algorithms, time complexity‐accuracy trade‐offs, and scalable system design. Our system has significant implications for enhancing email security, safeguarding users from security breaches, and mitigating the economic impacts of spam.

Predicting Electricity Consumption using an Innovative Deep Energy Predictor Model for Enhanced Accuracy and Efficiency

Accurate forecasting of electricity consumption is crucial for efficient energy management and planning. Traditional models often struggle to capture the complex, nonlinear relationships inherent in energy consumption data, leading to less reliable predictions. In the quest to improve the accuracy and efficiency of electricity consumption predictions, we introduce the Deep Energy Predictor Model (DEPM). This innovative hybrid model combines the strengths of XGBoost for classification and Deep Neural Networks (DNN) for deep learning (DL) capabilities. The model leverages advanced feature extraction techniques using Cascaded ResNet, ensuring the capture of intricate patterns within the data. Implemented in Python, the DEPM highlights an impressive accuracy of 98%, with a precision of 0.97, recall of 0.99, and an F1-score of 0.98, setting a new standard for predictive models in the energy sector. This study elaborates on the model architecture, implementation details, and the evaluation metrics that underscore the model's superior performance. Our results demonstrate the potential of DEPM to significantly enhance the reliability of electricity consumption forecasts, providing a robust tool for energy management and planning.

Cybersecurity on social media platforms: The use of deep learning methodologies for detecting anomalous user behavior

The exponential growth of social media platforms has brought unprecedented opportunities for global communication, networking, and information exchange. However, this expansion has also given rise to significant challenges, particularly in identifying and mitigating anomalous or malicious user behaviors such as spamming, cyberbullying, and misinformation dissemination. Traditional anomaly detection methods, which often rely on rule-based systems or basic statistical models, have proven inadequate in addressing the complex, dynamic, and evolving nature of user behavior on these platforms. In response, this paper explores the application of deep learning methodologies—specifically Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Autoencoders, and Generative Adversarial Networks (GANs)—for detecting anomalous behavior in social media environments. We present a novel deep learning architecture that integrates these models to enhance the detection accuracy of behavioral anomalies. By leveraging large-scale datasets and advanced feature extraction techniques, our approach demonstrates significant improvements over traditional methods, with higher precision, recall, and overall detection rates. The proposed model's ability to adapt to new and emerging patterns of behavior underscores its potential for real-world application in monitoring social media platforms. This research contributes to the growing body of literature on deep learning for cybersecurity and digital trust, offering a robust solution for maintaining the integrity of online social spaces. Our findings suggest that the implementation of these advanced methodologies can provide a more secure and reliable social media environment, benefiting both users and platform providers alike.

Utilization of Artificial Intelligence for the automated recognition of fine arts.

Fine art recognition, traditionally dependent on human expertise, is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and deep learning. This article introduces a novel AI-based approach for fine art recognition, utilizing Convolutional Neural Networks (CNNs) and advanced feature extraction techniques. Addressing the inherent challenges within this domain, we present a systematic methodology to enhance automated fine art recognition. By leveraging critical dataset characteristics such as objective type, genre, material, technique, and department, our method exhibits exceptional performance in classifying fine art pieces across diverse attributes. Our approach significantly improves accuracy and efficiency by integrating advanced feature extraction techniques with a customized CNN architecture. Experimental validation on a benchmark dataset highlights the efficacy of our method, indicating substantial contributions to the interdisciplinary field of fine art analysis.

Exploring the Impact of Image-Based Audio Representations in Classification Tasks Using Vision Transformers and Explainable AI Techniques

An important hurdle in medical diagnostics is the high-quality and interpretable classification of audio signals. In this study, we present an image-based representation of infant crying audio files to predict abnormal infant cries using a vision transformer and also show significant improvements in the performance and interpretability of this computer-aided tool. The use of advanced feature extraction techniques such as Gammatone Frequency Cepstral Coefficients (GFCCs) resulted in a classification accuracy of 96.33%. For other features (spectrogram and mel-spectrogram), the performance was very similar, with an accuracy of 93.17% for the spectrogram and 94.83% accuracy for the mel-spectrogram. We used our vision transformer (ViT) model, which is less complex but more effective than the proposed audio spectrogram transformer (AST). We incorporated explainable AI (XAI) techniques such as Layer-wise Relevance Propagation (LRP), Local Interpretable Model-agnostic Explanations (LIME), and attention mechanisms to ensure transparency and reliability in decision-making, which helped us understand the why of model predictions. The accuracy of detection was higher than previously reported and the results were easy to interpret, demonstrating that this work can potentially serve as a new benchmark for audio classification tasks, especially in medical diagnostics, and providing better prospects for an imminent future of trustworthy AI-based healthcare solutions.

Anxiety Detection System Based on Galvanic Skin Response Signals

Anxiety is a significant mental health concern that can be effectively monitored using physiological signals such as galvanic skin response (GSR). While the potential of machine learning (ML) algorithms to enhance the classification of anxiety based on GSR signals is promising, their effectiveness in this context remains largely underexplored. This study addresses this gap by investigating the performance of three commonly used ML algorithms, support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF), in classifying anxiety and stress activity using a benchmark dataset. We employed two feature extraction methods: traditional statistical feature extraction and an innovative automatic feature extraction approach utilizing a 14-layer autoencoder, aimed at improving classification performance. Our findings demonstrate the effectiveness of using GSR signals and the robust performance of the KNN algorithm in accurately classifying anxiety levels. The KNN algorithm achieved the highest accuracy in both the statistical and automatic feature extraction approaches, with results of 96.9% and 98.2%, respectively. These findings highlight the effectiveness of KNN for anxiety detection and emphasize the need for advanced feature extraction techniques to enhance classification outcomes in mental health monitoring.

Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques

Lung cancer remains a leading cause of cancer-related deaths globally. Early and accurate detection is crucial for improving patient outcomes. Traditional methods, relying on manual interpretation of medical images, are time-consuming and prone to errors. Deep learning, particularly convolutional neural networks (CNNs), offers an automated alternative capable of learning intricate patterns from medical images. However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. This paper presents a novel deep learning methodology that addresses these limitations. Our approach leverages expansive datasets, incorporates advanced feature extraction techniques, improves interpretability, and accommodates the diverse nature of lung cancer. Specifically, we develop dedicated models for both chest X-ray and CT images utilizing publicly available datasets from Kaggle. Through the integration of feature selection and model selection techniques—such as employing a genetic algorithm in conjunction with the tree-based pipeline optimization tool (TPOT)—we achieved remarkable accuracy. Our X-ray model attains an overall accuracy of 95.47%, the CT model achieves an accuracy of 98.70%, and the combined model achieves an impressive overall accuracy of 98.93%. Our methodology significantly enhances the performance and efficiency of lung cancer detection and is a valuable tool for early diagnosis and intervention.

Machine learning prediction of brain metastasis invasion pattern on brain magnetic resonance imaging scans.

Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a noninvasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination. From an initial cohort of 329 patients, a subset of 132 patients met the inclusion criteria for this retrospective study. We evaluated the ability of an expert neuroradiologist to reliably predict BMIP. Thereafter, the dataset was randomly divided into training/validation (80% of cases) and test subsets (20% of cases). The ground truth for BMIP was the histopathologic evaluation of resected specimens. Following MRI sequence co-registration, advanced feature extraction techniques deriving hand-crafted radiomic features with traditional ML classifiers and convolution-based deep learning (CDL) models were trained and evaluated. Different ML approaches were used individually or using ensembling techniques to determine the model with the best performance for BMIP prediction. Expert evaluation of brain MRI scans could not reliably predict BMIP, with an accuracy of 44%-59% depending on the semantic feature used. Among the different ML and CDL models evaluated, the best-performing model achieved an accuracy of 85% and an F1 score of 90%. ML approaches can effectively predict BMIP, representing a noninvasive MRI-based approach to guide the management of patients with brain metastases.

Mamba-VNPS: A Visual Navigation and Positioning System with State-Selection Space

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

Deep learning-driven ultrasound-assisted diagnosis: optimizing GallScopeNet for precise identification of biliary atresia.

Biliary atresia (BA) is a severe congenital biliary developmental abnormality threatening neonatal health. Traditional diagnostic methods rely heavily on experienced radiologists, making the process time-consuming and prone to variability. The application of deep learning for the automated diagnosis of BA remains underexplored. This study introduces GallScopeNet, a deep learning model designed to improve diagnostic efficiency and accuracy through innovative architecture and advanced feature extraction techniques. The model utilizes data from a carefully constructed dataset of gallbladder ultrasound images. A dataset comprising thousands of ultrasound images was employed, with the majority used for training and validation and a subset reserved for external testing. The model's performance was evaluated using five-fold cross-validation and external assessment, employing metrics such as accuracy and the area under the receiver operating characteristic curve (AUC), compared against clinical diagnostic standards. GallScopeNet demonstrated exceptional performance in distinguishing BA from non-BA cases. In the external test dataset, GallScopeNet achieved an accuracy of 81.21% and an AUC of 0.85, indicating strong diagnostic capabilities. The results highlighted the model's ability to maintain high classification performance, reducing misdiagnosis and missed diagnosis. GallScopeNet effectively differentiates between BA and non-BA images, demonstrating significant potential and reliability for early diagnosis. The system's high efficiency and accuracy suggest it could serve as a valuable diagnostic tool in clinical settings, providing substantial technical support for improving diagnostic workflows.

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

A Novel Approach of Traffic Congestion and Anomaly Detection with Prediction Using Deep Learning

Traffic congestion and anomalies present significant challenges to urban mobility, impacting economic activity and quality of life. Traditional traffic management systems often fall short in accurately predicting and mitigating these issues due to their reliance on static models and limited data sources. This study introduces a novel deep learning framework designed to enhance traffic congestion and anomaly detection and provide accurate traffic flow predictions. Leveraging a comprehensive dataset encompassing various traffic patterns and conditions, we employ a convolutional neural network (CNN) model, renowned for its efficacy in handling spatial data, combined with long short-term memory (LSTM) networks to capture temporal dependencies. Our approach distinguishes itself by incorporating real-time data and employing advanced feature extraction techniques, enabling the dynamic adjustment of traffic management strategies. The methodology section outlines the data preprocessing steps, model architecture, training process, and evaluation metrics employed. Experimental results demonstrate the model's superior performance over existing methods in terms of accuracy, precision, recall, and computational efficiency. The discussion elaborates on the model's practical implications for smart city initiatives and its potential to revolutionize traffic management systems. This study not only addresses the gaps identified in the literature review but also opens avenues for future research in applying deep learning to urban traffic challenges.

Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics

This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (SSC), Acidity and pulp percentage (PP) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the SSC, Acidity and PP in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the SSC (R2 = 0.9240, RMSE = 0.2595) and the PP (R2 = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting Acidity (R2 = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit.

An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction

Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.

Designing a Deep Autoencoder Neural Network for Detecting Sound Anomalies in Smart Factories Using Unsupervised Learning

Modern world technologies such as the integration of technologies such as the Internet of Things (IoT), cloud computing, and machine learning (ML) enhance the challenges of smart industrial management. Detecting anomalies in predictive maintenance within smart factories, and monitoring machine health to prevent unexpected breakdowns. This research presents an advanced model for designing automatic encoders capable of distinguishing between sounds emitted by machines in industrial environments and identifying faults. The MIMII dataset and advanced feature extraction techniques, such as MFCCs, are adopted as key factors in making the proposed model. The four evaluation measures: accuracy, recall, recall, and F1 score, in addition to the confusion matrix, were also adopted. To evaluate the model's performance. The results confirm the effectiveness and robustness of the proposed deep neural network model designed for autoencoders in the field of artificial audio classification. With a commendable accuracy rate of 93.95% and F1 score of 95.31%,

Advances in AI for automatic sign language recognition: A comparative study of machine learning approaches

Sign language recognition is a vital field within artificial intelligence, aiming to bridge communication gaps between deaf or hard-of-hearing communities and others by translating visual gestures into text or speech. Automatic Sign Language Recognition (ASLR) systems seek to interpret these complex and nuanced gestures with greater accuracy, expanding access to information communicated through sign language. This paper presents a comparative review of machine learning methods used in ASLR, emphasizing their impact on improving communication for hearing-impaired individuals. It also explores the primary challenges in ASLR, such as variability in signs and the complexities of gesture recognition. Advanced feature extraction techniques like SIFT, HOG, and SURF are examined for their role in enhancing ASLR system performance. Additionally, a bibliometric study highlights significant trends and advancements in intelligent systems for sign language recognition over the past two decades. This paper synthesizes recent research on ASLR technologies, supporting the development of more effective communication tools and fostering social inclusivity for deaf and hard-of-hearing communities.