Learning Approach Research Articles

The investigation of traditional models and machine learning models in dynamic facial expression recognition

In everyday life, dynamic facial expressions are merely continuous human responses to external events. However, in human-computer interaction, rapidly recognizing changes in facial expressions from video streams is a relatively complex process. This complexity renders Dynamic Facial Expression Recognition (DFER) a critical research task in the domains of computer vision and image processing. This paper analyses the correlations and contrasts between static and dynamic facial expression research, highlighting key issues in the study of dynamic facial expressions, such as dynamic feature extraction and frame extraction. After that, it enumerates significant algorithms in both traditional models and deep learning models, providing an analysis of the advantages and disadvantages of these two major approaches. At the same time, it investigates the reasons behind the transition of research models for DFER from traditional methods to deep learning approaches. The paper focuses on two notable models from each approach: Histogram of Oriented Gradient (HOG) for processing raw images, Support Vector Machine (SVM) for data classification in traditional models. Convolutional Neural Network (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal feature extraction in deep learning models. These models are discussed in detail concerning their strengths and weaknesses, operational processes, and performance outcomes. In the concluding section, the author summarizes the main factors influencing research in this field and the current challenges encountered. By focusing on future research directions, the paper also presents a review of recent methodologies and offers insightful research directions for further investigation.

Optimization of basic chemistry learning: Validation of stem-based problem-based learning materials for chemical equilibrium

Background: The objective of this study is to create Basic Chemistry learning materials focused on the topic of Chemical Equilibrium using a STEM Problem-Based Learning approach. Effective learning is necessary to enhance students' understanding of complex chemistry concepts. Method: This study adopts the STEM Problem-Based Learning material development method, utilizing the ADDIE model, which consists of five steps: analysis, design, development, implementation, and evaluation. After the development process, the learning materials were assessed through an expert review stage. Evaluation was conducted to assess the validity of the design, pedagogical validity, and content validity using Aiken's coefficient as an indicator. Results: The expert review evaluation results showed high Aiken's coefficients: 0.98 for design validity, 0.95 for pedagogical validity, and 0.96 for content validity. These findings confirm that the developed learning materials comprehensively meet validity standards. Conclusion: Based on these evaluation results, it can be concluded that the developed Basic Chemistry learning materials, particularly on Chemical Equilibrium using the STEM Problem-Based Learning approach, meet the validity criteria. Therefore, these learning materials are considered suitable for use in the Basic Chemistry learning process. Novelty/Originality of this study: This study integrates the STEM Problem-Based Learning approach into developing Basic Chemistry learning materials, specifically on Chemical Equilibrium. Using the ADDIE model and comprehensive validation, this study presents a new framework for designing learning materials that simultaneously meet design, pedagogical, and content standards.

Advancements in intelligent driving assistance: A machine learning approach to identify real-time driving strategies using environmental, eye movement, control-related, and kinetic-related data

Human-vehicle collaboration (HVC) is gaining focus as intelligent vehicle technology shifts driving responsibilities from humans to automation. Effective HVC hinges on mutual understanding between parties. Driving strategies address specific driving requirements and guide maneuver decisions, offering a gateway to uncovering a driver’s immediate needs. The present study introduces the framework of adaptive driving (FAD), which uses machine learning algorithms to incorporate critical features for identifying four common driving strategies, including competitive driving, skill-based behavior, defensive driving, and urgent reactions. A total of 52 participants engaged in simulated driving sessions, with these sessions yielding 983 driving events that were used for model training. Discriminative models considering driving environment, eye movement, or combined features can satisfactorily classify four driving strategies, while control-related and kinetic-related data offer minor contributions. Environmental data significantly aids in identifying skill-based and competitive strategies, characterized by lower risk dynamics and longer Time-to-Collision (TTC), with forward targets often triggering competitive driving. For risk mitigation strategies, both environment and eye movement data are crucial. Scenarios with higher risk dynamics prompt defensive driving, while shorter TTC indicates urgent reactions. Defensive driving is revealed by saccade patterns and less variation in pupil activities, whereas urgent reactions show pronounced pupil dilation and reduced saccade activity, indicating challenges in environmental observation during emergencies.

Person Re-ID: Tackling occlusion, labeled data, and privacy

Person re-identification (ReID) has become a critical component in various security and surveillance systems, necessitating accurate and robust identification of individuals across different camera views. The motivation for enhancing ReID systems stems from the need to improve public safety, prevent crime, and support forensic investigations. Despite significant advances, ReID faces several key challenges that hinder its deployment in real-world applications. This review paper addresses key challenges in person re-identification (ReID): overcoming occlusion, reducing dependence on labeled data, and addressing privacy concerns. Various techniques have been explored to tackle these issues effectively. For occlusion, part-based models, LCNNs, attention mechanisms, and adversarial training GANs have demonstrated robustness in capturing person features across diverse and complex environments. To mitigate dependence on labeled data, semi-supervised and unsupervised learning approaches leverage unlabeled data and self-learning capabilities. Additionally, multimodal fusion utilizes complementary information from different sources, significantly enhancing model generalization and performance. Privacy concerns are addressed through federated learning, which fosters collaboration across devices while safeguarding individual data privacy. In summary, this paper highlights diverse technological applications in ReID, offering valuable insights and guidance for future research and practical implementations in the field.

Deep Learning Approach to Detect Fake Video on Raspberry PI

Abstract: This project proposes a deep learning-based approach for real-time detection of fake videos on a resource-constrained device, specifically the Raspberry Pi. The solution combines the power of computer vision and recurrent neural networks to discern manipulated content from authentic videos effectively. The methodology involves using a pre-trained ResNeXt model for feature extraction, capturing spatial information from each video frame. These features are then fed into a Long Short-Term Memory (LSTM) network, allowing the model to understand and exploit temporal dependencies within the sequence of frames. The LSTM network learns patterns and nuances indicative of authentic or manipulated video content. The training process involves a carefully curated dataset containing both real and fake videos. The model is fine-tuned to optimize its performance, and metrics such as accuracy, precision, recall, and F1 score are employed for evaluation. To accommodate the constraints of the Raspberry Pi, the model is further optimized through techniques such as quantization, ensuring a balance between model size and inference accuracy. The final model is deployed on the Raspberry Pi, with a user-friendly interface capturing video frames in real-time. The system provides instantaneous feedback, indicating whether the observed video content is genuine or manipulated. This project contributes to the growing field of deepfake detection while addressing the challenges of implementing sophisticated models on edge devices. The combination of ResNeXt and LSTM offers a robust solution for discerning manipulated videos, making it suitable for real-world applications where computational resources are limited.

Attention-enhanced dilated convolution for Parkinson’s disease detection using transcranial sonography

BackgroundTranscranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.MethodsThis study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.ResultsThe study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.ConclusionThe AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.Graphical

Exploring protein relative relations in skeletal muscle proteomic analysis for insights into insulin resistance and type 2 diabetes

The escalating prevalence of insulin resistance (IR) and type 2 diabetes mellitus (T2D) underscores the urgent need for improved early detection techniques and effective treatment strategies. In this context, our study presents a proteomic analysis of post-exercise skeletal muscle biopsies from individuals across a spectrum of glucose metabolism states: normal, prediabetes, and T2D. This enabled the identification of significant protein relationships indicative of each specific glycemic condition. Our investigation primarily leveraged the machine learning approach, employing the white-box algorithm relative evolutionary hierarchical analysis (REHA), to explore the impact of regulated, mixed mode exercise on skeletal muscle proteome in subjects with diverse glycemic status. This method aimed to advance the diagnosis of IR and T2D and elucidate the molecular pathways involved in its development and the response to exercise. Additionally, we used proteomics-specific statistical analysis to provide a comparative perspective, highlighting the nuanced differences identified by REHA. Validation of the REHA model with a comparable external dataset further demonstrated its efficacy in distinguishing between diverse proteomic profiles. Key metrics such as accuracy and the area under the ROC curve confirmed REHA’s capability to uncover novel molecular pathways and significant protein interactions, offering fresh insights into the effects of exercise on IR and T2D pathophysiology of skeletal muscle. The visualizations not only underscored significant proteins and their interactions but also showcased decision trees that effectively differentiate between various glycemic states, thereby enhancing our understanding of the biomolecular landscape of T2D.

Single annotator versus multi-annotator: Challenge of segmenting two neighboring hippocampus head and body with high precision

Background and objectiveThis work investigates multi-annotator segmentation framework for semantic image segmentation using deep learning approaches. MethodsThe framework consists of various deep learning models which perform independently, and then their decisions are fused by a combiner network. The underlying idea of the multi-annotator (ensemble) segmentation framework is to take advantage of the synergistic effect created by the complementary information provided by the different annotators to reach a peak performance superior to each of the annotators when performing alone. The performance of the multi-annotator framework was assessed for the challenging task of the hippocampus head and body segmentation from MR images. Atlas-based segmentation method, deep learning-based multi-view segmentation approach, as well as different decision fusion algorithms, such as STAPLE and shape-based averaging, were assessed versus the proposed multi-annotator segmentation framework. ResultsAmong the different deep learning architectures and loss functions, ResNet model with cross-entropy loss function (ResNet-CE) exhibited the best performance (best performing single model) with a Dice of 88.6 ± 1.8 % and 88.6 ± 1.8 % for the hippocampus head and body, respectively. However, the multi-annotator segmentation framework (which also involved Resnet-CE as one of the annotators) exhibited superior performance with Dice of 91.1 ± 1.3 % (head) and 91.0 ± 1.3 % (body) compared to the atlas-based method with Dice of 88.5 ± 1.5 % (head) and 88.4 ± 1.5 % (body) and multi-view approach with Dice of 89.0 ± 1.4 % (head) and 88.9 ± 1.5 % (body). ConclusionThe multi-annotator segmentation framework exhibits superior performance over each annotator alone which could be employed almost for all machine learning-related problems to enhance the overall effectiveness of the deep/machine learning algorithms.

Controlled Porosity of Selective Laser Melting-Produced Thermal Pipes: Experimental Analysis and Machine Learning Approach for Pore Recognition on Pipes Surfaces

This study investigates the methods for controlling porosity in thermal pipes manufactured using selective laser melting (SLM) technology. Experiments conducted include water permeability tests and surface roughness measurements, which are complemented by SEM image ML-based analysis for pore recognition. The results elucidate the impact of SLM printing parameters on water permeability. Specifically, an increase in hatch and point distances leads to a linear rise in permeability, while higher laser power diminishes permeability. Using machine learning (ML) techniques, precise pore identification on SEM images depicting surface microstructures of the samples is achieved. The average percentage of the surface area containing detected pores for microstructure samples printed with laser parameters (laser power (W) _ hatch distance (µm) _ point distance (µm)) 175_ 80_80 was found to be 5.2%, while for 225_120_120, it was 4.2%, and for 275_160_160, it was 3.8%. Pore recognition was conducted using the Haar feature-based method, and the optimal patch size was determined to be 36 pixels on monochrome images of microstructures with a magnification of 33×, which were acquired using a Leica S9 D microscope.

Improved Sparse Representation of Image from Inferred Angles of Steerable Wavelet

Objectives: Presenting images with sparse coefficients has a wide variety of real-time applications in compressive sensing. However, sparse representations of images present challenges due hidden similarities in the higher order moments. Literature suggests that the applications that involve natural images present a high level of similarity. Steerable basis, due to their rotational invariant property, have shown potential in sparse representation of natural images. Hence, the objective of the proposed study is to identify steerable basis that maximize the sparse representation of natural images. Method: Prior studies have used the angle of steerable basis either from the random assignment or derived from Hough transform. In this study, we propose the selection of steerable basis angle derived from maximum a prior method. Exploiting a steerable basis for better sparse representation requires the knowledge of proper steerable angles. Hence, we propose using MAP learning approach to identify this angle. Findings: The proposed method resulted in optimal steerable angle without the need for calculation of Hough Transform. In addition, the method also resulted in almost 10 percent improvement in sparse representation as indicated by higher Kurtosis. Novelty: We compare the measure of sparsity to evaluate the effectiveness of the proposed method. The results indicate the optimal sparsity from the proposed method as indicated from the maximum values of kurtosis compared to the previous related methods. In addition, the proposed method relaxes the requirement of manipulating Hough transform for optimal steerable angle. Keywords: Sparsity, Steerable Basis, Wavelet Pyramid Structure, Image Compression, Hough Transform

Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

Challenges in Student Centered Learning

Abstract: In a student- centered approach the student is at the center of student-centered learning, where learning is dependent upon the learner's interest. It empowers learners to take ownership of their learning process, encouraging and experience real life situations. The goal of this project is to determine the challenges schools, teachers and students are facing in adopting modern technologies. The author discussed the types of student-centered learning and how a teacher can gain attention of students while using gadgets. The Prons and cons of applying student focused learning is mentioned in the article. It also shows how it is different from traditional ways of teaching and its changes are beneficiary for student and teacher. Therefore, NEP 2020 emphasizes education for all children but on the needs of the student rather than the teacher. The scaffolding means teacher helps students through their guidance, but the completion of activity is done by students. in student learning approach the student not only enhances the confidence but also use the cognitive and abstract thinking. This approach also develops the leadership skills of students and brings harmony among peer groups. The challenge teachers are facing is to use individualized plans for every diverse need of students. There is an enormous difference between student centered and traditional ways of teaching. The students are given opportunities to explore more and focus on practical work. Students become more responsible for their future course of action. In student centered learning, the students do abstarct thinking and teachers take them to field work for real life experiences. Many different commissions are developed by government of India to engage every student in practical world and give them opportunities to become a successful citizen. E- learning is the best example for student involvement in online mode with their peer group. Some of the tasks are given online by a teacher like making PPT whitehanded the computer skills and confidence of student in giving presentation.

Machine Learning Models for Predictive Maintenance in Industrial Engineering

Purpose: The general objective of this study was to investigate machine learning models for predictive maintenance in industrial engineering. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning models for predictive maintenance in industrial engineering. The research highlighted the transformative potential of machine learning models in optimizing predictive maintenance for industrial engineering, demonstrating significant reductions in unplanned downtime and maintenance costs. It identified the strengths of various machine learning approaches, such as supervised, unsupervised, and reinforcement learning, in predicting equipment failures and optimizing maintenance schedules. Despite the benefits, challenges such as data quality, integration complexity, and the need for specialized skills were noted. Future advancements in machine learning, IoT data, and computational power were expected to further enhance predictive maintenance systems, making them more accurate, efficient, and widely adopted across industries.Unique Contribution to Theory, Practice and Policy: The Systems Theory, Diffusion of Innovations Theory and Resource-Based View (RBV) Theory may be used to anchor future studies on machine learning models for predictive maintenance in industrial engineering. This study provided several recommendations that contributed to theory, practice, and policy. It emphasized the development of hybrid machine learning models, integration of domain-specific knowledge, and real-time data collection using IoT technologies. It suggested standardized data protocols and personnel training for better implementation and efficiency. Policy recommendations included regulatory frameworks, incentives for technology adoption, data sharing, and robust data privacy guidelines. These contributions aimed to enhance the accuracy and applicability of predictive maintenance models, improve industrial maintenance practices, and support technological innovation through supportive policies.

“Machine Learning Approaches to Enhance Candidate Selection: A Comparative Study in HR Recruitment"

“Machine Learning Approaches to Enhance Candidate Selection: A Comparative Study in HR Recruitment"

Educational practices for the language development of students with intellectual developmental disorder in the school setting: a systematic review

BackgroundIntellectual development disorder (IDD) is a challenging disorder that professionals, most notably teachers, are confronted with daily. Supporting oral language in the classroom is a complex challenge as teachers are expected to implement effective methods to support students’ language. However, they face a persistent scarcity of scientific evidence to draw upon in their practice.AimsThis research provides a first overview of instructional methods and their effectiveness in this population.MethodOur search was conducted through various databases, generating a total of 931 articles. Data from the 14 selected studies were analyzed.Outcomes and resultsThe results reveal limited, heterogenous, and hardly comparable studies in terms of their characteristics (samples, language area, strategies, and effectiveness measures) suggesting a low level of evidence regarding the effectiveness of these practices.Conclusion and implicationsWe discuss the implications of these findings in light of the different factors contributing to the interventions’ effectiveness, as well as considering the issue of transfer and generalization of acquired skills in IDD. We also reveal the urgent need for developing methods addressing cognitive load limitations such as implicit learning approaches, to enhances effectiveness and compensate the gap of available resources tailored to improve oral language outcomes in the IDD population.

Machine Learning Approaches for Network Intrusion Detection: An Evaluation of their Efficacy in Bolstering Security

Abstract: In today's digital environment, securing networked systems is critical due to the increasing sophistication and frequency of cyberattacks. Network Intrusion Detection Systems (NIDS) are essential for detecting and addressing unauthorized and harmful activities within a network. NIDS function by continuously monitoring network traffic and analyzing data packets for signs of suspicious behavior or known attack patterns. This paper provides an in-depth examination of NIDS, focusing on the application of three machine learning algorithms—K Neighbors Classifier, Logistic Regression, and Random Forest Classifier—to create a robust model for network intrusion detection using the NSL-KDD dataset. Our findings highlight the superior performance of the Random Forest Classifier, which achieved an accuracy of 99.31%, proving its effectiveness in differentiating between normal and malicious traffic. The analysis of ICMP, TCP, and UDP protocols reveals unique attack patterns, underscoring the need for protocol-specific security measures. Additionally, the study emphasizes the importance of integrating NIDS with other security systems for a multi-layered defense strategy and the crucial role of skilled personnel in managing and interpreting NIDS alerts. The results advocate for ongoing innovation and adaptation in NIDS technologies and strategies to effectively counter evolving cyber threats

Identification of genetic susceptibility for Chinese migraine with depression using machine learning

BackgroundMigraine is a common primary headache that has a significant impact on patients’ quality of life. The co-occurrence of migraine and depression is frequent, resulting in more complex symptoms and a poorer prognosis. The evidence suggests that depression and migraine comorbidity share a polygenic genetic background.ObjectiveThe aim of this study is to identify related genetic variants that contribute to genetic susceptibility to migraine with and without depression in a Chinese cohort.MethodsIn this case-control study, 263 individuals with migraines and 223 race-matched controls were included. Eight genetic polymorphism loci selected from the GWAS were genotyped using Sequenom’s MALDI-TOF iPLEX platform.ResultsIn univariate analysis, ANKDD1B rs904743 showed significant differences in genotype and allele distribution between migraineurs and controls. Furthermore, a machine learning approach was used to perform multivariate analysis. The results of the Random Forest algorithm indicated that ANKDD1B rs904743 was a significant risk factor for migraine susceptibility in China. Additionally, subgroup analysis by the Boruta algorithm showed a significant association between this SNP and migraine comorbid depression. Migraineurs with depression have been observed to have worse scores on the Beck Anxiety Inventory (BAI) and the Migraine Disability Assessment Scale (MIDAS).ConclusionThe study indicates that there is an association between ANKDD1B rs904743 and susceptibility to migraine with and without depression in Chinese patients.

Federated learning: Impact of different algorithms and models on prediction results based on fashion-MNIST data set

The realm of federated learning is rapidly advancing amid the era of big data. Therefore, how to select a suitable federated learning algorithm to achieve realistic tasks has become particularly critical. In this study, we explore the impact of different algorithms and models on the prediction results of Federated Learning (FL) using the Fashion-MNIST data set. Federated Learning enhances data privacy and reduces latency by training models directly on local devices since it is a decentralized machine learning approach. We analyze the performance of several FL algorithms including Federated Averaging (FedAvg), Federated Stochastic Gradient Descent (FedSGD), Federated Proximal (FedProx), and SCAFFOLD. Our experiments reveal significant differences in accuracy and stability among these algorithms, highlighting their strengths and weaknesses in handling non-IID (Non-Independent and Identically Distributed) data. FedProx demonstrate superior performance in terms of accuracy and robustness, making them suitable for complex federated learning environments. These discoveries offer crucial insights for choosing suitable FL algorithms and models in practical applications.

Data‐driven quality prediction in injection molding: An autoencoder and machine learning approach

AbstractIn the injection molding process, the pressure within the mold cavity is crucial to the quality of the final product. Due to the inability to directly observe the process, sensor technology is required to acquire data. Traditionally, experts interpret and encode pressure curves, but this method has limitations. This study proposes an innovative pressure curve encoding technique to overcome these limitations and achieve automation to obtain more comprehensive pressure information. The study employs mold flow analysis software and autoencoders to capture and encode pressure data, classifying pressure curves into global pressure and local pressure values. Subsequently, a multilayer perceptron (MLP) neural network is used for machine learning to predict multiple qualities. Results indicate that local pressure features perform better in predicting multiple‐quality targets than global pressure features, exhibiting smaller prediction ranges and higher prediction stability. Although domain knowledge‐based indicator features slightly outperform in terms of predictive capability, the low error results of the local pressure feature method validate the effectiveness of the autoencoder approach, demonstrating its potential for digital information extraction and practical quality prediction in the injection molding process.Highlights Develops a product quality prediction system for efficient injection molding. Autoencoders extract key features from pressure data without domain knowledge. ML models predict quality indicators, optimizing injection molding processes. Compares pressure features, showing data‐driven methods' prediction accuracy.

Sentiment analysis with machine learning and deep learning: A survey of techniques and applications

Sentiment analysis is the task of automatically identifying the sentiment expressed in text. It has become increasingly important in many applications such as social media monitoring, product reviews analysis, and customer feedback evaluation. With the advent of deep learning techniques, sentiment analysis has seen significant improvements in performance and accuracy. This paper presents a comprehensive survey of machine learning and deep learning methods for sentiment analysis at the document, sentence, and aspect levels. We first provide an overview of traditional machine learning approaches to sentiment analysis and their limitations. We then look into various machine learning and deep learning architectures that have been successfully applied to this task. Additionally, we discuss the challenges of dealing with different data modalities, such as visual and multimodal data, and how both techniques have been adapted to address these challenges. Furthermore, we explore the applications of sentiment analysis in diverse domains, including social media, product reviews, and healthcare. Finally, we highlight the current limitations of deep learning approaches for sentiment analysis and outline potential future research directions. This survey aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art deep learning techniques for sentiment analysis and their practical applications.