Effective Facial Expression Recognition System Using Artificial Intelligence Technique DOI Creative Commons

Imad S. Yousif,

Tarik A. Rashid, Ahmed S. Shamsaldin

et al.

Kurdistan Journal of Applied Research, Journal Year: 2024, Volume and Issue: 9(2), P. 117 - 130

Published: Dec. 30, 2024

Facial expressions are the most basic non-verbal method people use to communicate feelings, intentions and reactions without words. Recognizing these facial accurately is essential for a variety of applications — such as tools that our faces interact with computers (human-computer interaction, or HCI), security systems emotionally intelligent artificial intelligence technologies. As complexities surrounding relationships have become better understood, it has allowed us develop increasingly more complex identifying detecting different emotions. This paper presents an improved performance Expression Recognition (FER) via augmentation in Artificial Neural Networks Genetic Algorithms, two renowned techniques possessing disparate strengths. ANNS inspired by neural architecture human brain capable learning recognizing patterns unchartered data after trained examples, on other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, mutation. The research effort mitigate problems pertaining conventional methods, like overfitting generalization fault order design FER model potential performing much accurately. A hybrid ANN-GA uses Petri Nets production proposed real-time video sequence analysis high precision predicting dynamic activities anger, surprise, disgust, joy, sadness fear emotion faces. Importantly, results study show this integrated large-scale promoting effect detection upon varied scenes therefore generalizable many domains surveillance over biomedicine up interactive AI-driven systems. Implications implementing context-aware recognition emotions based AI technologies far-reaching they demonstrate offer at enhancing deciphering.

Language: Английский

A Scoping Review of Literature on Deep Learning Techniques for Face Recognition DOI Creative Commons
Andisani Nemavhola, Serestina Viriri, Colin Chibaya

et al.

Human Behavior and Emerging Technologies, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Deep learning has led to the creation of facial recognition technologies using convolutional neural networks (CNNs). This preliminary study explores application CNN architectures in face gain a deeper understanding challenges and methodologies field. The systematically reviewed relevant literature Preferred Reporting Items for Systematic reviews Meta‐Analyses extension Scoping Reviews (PRISMA‐ScR) framework. Out 3622 eligible papers, 266 were included review, with 47% proposing new techniques 1% focusing on method implementation comparison. Most studies used images rather than video as training or testing data, 78% clean data only 7% utilizing occluded data. It was observed that traditional predominantly employed. identified lack research definition architectures, development models both videos, exploration nontraditional architectures. affecting occlusion, distance from camera, camera angle, lighting conditions. assessment provides an insight into use suggests could be further explored future research.

Language: Английский

Citations

1
A Comprehensive Survey of Deep Learning Approaches in Image Processing DOI Creative Commons
Μαρία Τρίγκα, Ηλίας Δρίτσας

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 531 - 531

Published: Jan. 17, 2025

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach traditional methodologies. This survey offers an in-depth exploration DL approaches that have redefined processing, tracing their evolution from early innovations to latest state-of-the-art developments. It also analyzes progression architectural designs and paradigms significantly enhanced ability process interpret complex visual data. Key such as techniques improving model efficiency, generalization, robustness, are examined, showcasing DL's address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous evaluation discussed, underscoring importance performance assessment in varied application contexts. impact is highlighted through its tackle challenges generate actionable insights. Finally, this identifies potential future directions, including emerging technologies like quantum computing neuromorphic architectures efficiency federated privacy-preserving training. Additionally, it highlights combining with edge explainable artificial intelligence (AI) scalability interpretability challenges. These advancements positioned further extend applications DL, driving innovation processing.

Language: Английский

Citations

1
Stakeholder Interactions and Ethical Imperatives in Big Data and AI Development DOI Creative Commons
Jarosław Brodny, Magdalena Tutak

Journal of Open Innovation Technology Market and Complexity, Journal Year: 2025, Volume and Issue: unknown, P. 100491 - 100491

Published: Feb. 1, 2025

Language: Английский

Citations

0
Criminal Justice System in the Age of Artificial Intelligence DOI
Showkat Ahmad Wani, Sheikh Inam Ul Mansoor

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 67 - 92

Published: Feb. 28, 2025

AI biases can induce existing imbalances and affect the most affected populations more severely. The study underlines need to introduce imperative of transparency explainability systems. fact that many algorithmic systems are correspondence opaque raises questions about how such decisions made who is accountable when using artificial intelligence, which leads wrongful arrest or unfair sentencing. research calls for effective legislative frameworks would protect constitutional entitlement due widespread use criminal justice system effectively embrace avoid risk infringing individual rights make technology serve rather than inimical detrimental basic human rights.

Language: Английский

Citations

0
Exploring the prospects of multimodal large language models for Automated Emotion Recognition in education: Insights from Gemini DOI
Shuzhen Yu, Alexey Androsov, Hanbing Yan

et al.

Computers & Education, Journal Year: 2025, Volume and Issue: unknown, P. 105307 - 105307

Published: March 1, 2025

Language: Английский

Citations

0
Ethical Implications of Artificial Intelligence in University Education DOI Open Access

Jared Momanyi Mauti,

Dennis Song’oro Ayieko

East African Journal of Education Studies, Journal Year: 2025, Volume and Issue: 8(1), P. 159 - 167

Published: Jan. 3, 2025

The integration of Artificial Intelligence (AI) in university education has emerged as a transformative force, promising to revolutionize teaching, learning, and administration. However, its rapid adoption sparked ethical concerns, particularly resource-constrained settings. This theoretical article examines the implications specific AI applications, including plagiarism detection tools, adaptive learning systems, automated grading technologies within Kenyan universities. It highlights three critical areas: data privacy security, student-lecturer dynamics, algorithmic bias. Drawing from Kantian deontological ethics, which emphasizes duty inherent morality actions, argues for balanced approach that prioritizes responsibilities over mere technological expedience. Data security remain pivotal systems amass extensive personal data, often without robust safeguards, exposing students potential exploitation breaches. explores intersection relationships, revealing how AI-driven tools can disrupt traditional mentorship roles central African pedagogical traditions. Furthermore, pervasive issue bias is critically analysed, emphasizing perpetuate educational inequities marginalize underrepresented groups. absence localized frameworks address these dilemmas By anchoring analysis this provides compelling framework navigating challenges posed by education, ensuring implementation enhances equity, accountability, human dignity. work contributes ongoing discourse on responsible use offering actionable insights policy, research, practice

Language: Английский

Citations

0
The Impact of Deep Fakes in Markets and Economies DOI
Iris-Panagiota Efthymiou,

Theocharis Efthymiou Egleton

Advances in business information systems and analytics book series, Journal Year: 2024, Volume and Issue: unknown, P. 19 - 50

Published: Dec. 5, 2024

The advent of deepfake technology has introduced significant challenges and opportunities in markets economies globally. This paper examines the multifaceted impact deepfakes on financial markets, corporate reputations, consumer behaviour, economic stability. By synthesizing recent case studies academic research, we explore how can manipulate stock prices, erode trust brands, influence market decisions, leading to potential disruptions. We also discuss role regulatory frameworks, technological countermeasures, ethical considerations mitigating risks posed by deepfakes. Our analysis highlights urgent need for enhanced vigilance, cross-sector collaboration, innovative solutions safeguard integrity stability face this emerging threat.

Language: Английский

Citations

1
Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion DOI Creative Commons
Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra,

Pablo Pérez-Lázaro

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(24), P. 2886 - 2886

Published: Dec. 22, 2024

Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality quality of life. The early detection these diseases is challenging, often relying on in-person screening, which difficult to implement regularly. This study aims develop artificial intelligence algorithms based gait analysis, integrating sensor computer vision (CV) data, detect sarcopenia CD. Methods: A cross-sectional case-control was conducted involving 42 individuals aged 60 years or older. Participants were classified as having if they met the criteria established by European Working Group Older People CD their score Mini-Mental State Examination ≤24 points. Gait patterns assessed at usual walking speeds using sensors attached feet lumbar region, CV data captured a camera. Several key variables related dynamics extracted. Finally, machine learning models developed predict Results: Models combination both technologies achieved high predictive accuracy, particularly for best model an F1-score 0.914, with 95% sensitivity 92% specificity. combined also demonstrated performance, yielding 0.748 100% 83% Conclusions: demonstrates that analysis through fusion can effectively screen multimodal approach enhances potentially supporting disease intervention home settings.

Language: Английский

Citations

0
Exploring Applications and Implications of Big Data Predictive Analytics in Policing Cyberspace DOI

Joel Pinney,

Vibhushinie Bentotahewa, Matt Tomlinson

et al.

Advanced sciences and technologies for security applications, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: Nov. 26, 2024

Language: Английский

Citations

0
Effective Facial Expression Recognition System Using Artificial Intelligence Technique DOI Creative Commons

Imad S. Yousif,

Tarik A. Rashid, Ahmed S. Shamsaldin

et al.

Kurdistan Journal of Applied Research, Journal Year: 2024, Volume and Issue: 9(2), P. 117 - 130

Published: Dec. 30, 2024

Facial expressions are the most basic non-verbal method people use to communicate feelings, intentions and reactions without words. Recognizing these facial accurately is essential for a variety of applications — such as tools that our faces interact with computers (human-computer interaction, or HCI), security systems emotionally intelligent artificial intelligence technologies. As complexities surrounding relationships have become better understood, it has allowed us develop increasingly more complex identifying detecting different emotions. This paper presents an improved performance Expression Recognition (FER) via augmentation in Artificial Neural Networks Genetic Algorithms, two renowned techniques possessing disparate strengths. ANNS inspired by neural architecture human brain capable learning recognizing patterns unchartered data after trained examples, on other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, mutation. The research effort mitigate problems pertaining conventional methods, like overfitting generalization fault order design FER model potential performing much accurately. A hybrid ANN-GA uses Petri Nets production proposed real-time video sequence analysis high precision predicting dynamic activities anger, surprise, disgust, joy, sadness fear emotion faces. Importantly, results study show this integrated large-scale promoting effect detection upon varied scenes therefore generalizable many domains surveillance over biomedicine up interactive AI-driven systems. Implications implementing context-aware recognition emotions based AI technologies far-reaching they demonstrate offer at enhancing deciphering.

Language: Английский

Citations

0