Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Haq, Hafiz Burhan Ul; Saqlain, Muhammad

doi:10.56578/jii010301

Cited by 8 publications

(9 citation statements)

References 21 publications

(25 reference statements)

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“…Cai et al [17] introduced a real-time FPGA accelerator based on the Winograd algorithm, emphasizing the importance of hardware acceleration for efficient underwater object detection. Haq & Saqlain [18] placed a critical emphasis on precision, while Zulqarnain & Saqlain [19] identified text readability. Collectively, these studies represent a diverse set of approaches, methodologies, and technological considerations in the realm of underwater object detection, showcasing the multidisciplinary efforts in advancing the field.…”

Section: Related Workmentioning

confidence: 99%

Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network

Baig,

Ul Haq

2024

Decis. Mak. Adv.

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This research presents an innovative application of the YOLOv4 object detection model for the identification and classification of marine objects within a dataset encompassing seven distinct classes. The study focuses on enhancing the robustness and accuracy of object detection in challenging marine environments, leveraging the unique capabilities of YOLOv4. Pre-processing steps involve resizing raw images, applying data augmentations, and normalizing pixel values to ensure optimal model training. Specifically tailored for underwater scenarios, additional color space transformations address variations in lighting conditions. The model is trained to detect marine objects such as fish, corals, and underwater structures, contributing to advancements in underwater exploration, environmental monitoring, and marine resource management. Experimental results demonstrate the effectiveness of the proposed approach, showcasing YOLOv4's ability to accurately identify and classify marine objects across the specified seven classes. This research not only expands the applicability of YOLOv4 in the marine domain but also provides valuable insights for the development of intelligent systems for underwater object detection.

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Section: Related Workmentioning

confidence: 99%

Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network

Baig,

Ul Haq

2024

Decis. Mak. Adv.

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“…Figure 4 shows the specific model architecture. Many approaches to solving such challenges have been proposed by the studies (Abid & Saqlain, 2023;Haq & Saqlain, 2023a;Haq & Saqlain, 2023b;Saqlain, 2023;Saqlain & Xin, 2020;Saqlain et al, 2020).…”

Section: Theory Of Deep Learningmentioning

confidence: 99%

Interplay of Government Support and Foreign Investment in Enhancing R&D in China’s Strategic Sectors

Imran,

Rehman

2024

JOTE

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This investigation explores the intricate relationship between government support, foreign investment, and research and development (R&D) commitment in China's strategic sectors, with a focus on the crucial role of government assistance in spurring innovation in small-scale enterprises. Utilizing advanced deep learning and data mining techniques, the impact of government subsidies and tax incentives on R&D dedication in burgeoning strategic firms is meticulously analyzed, particularly in the context of varying levels of external financing. The findings reveal a significant, multifaceted influence of government financial aid and foreign capital on R&D endeavors. A pronounced, positive correlation between government financial backing and R&D activities emerges, with the magnitude of this relationship being substantially modulated by the degree of external investment. This underscores the importance of strategic financial orchestration in nurturing research and innovation within pivotal sectors. Insights gleaned from this study are of paramount importance for policy formulators and business leaders, offering a refined comprehension of the synergistic interplay between government incentives, foreign investment, and R&D commitment. These findings are pivotal for promoting economic growth and technological progress in China's key industries.

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“…The model's effectiveness is evaluated through metrics such as F1-score, recall, and precision, with respective values of 84.07%, 78.22%, and 94.09%. Additionally, numerous studies employing machine learning methods have contributed to this field [21][22][23][24][25][26][27]. Despite advancements, certain facial recognition approaches encounter challenges, including poor lighting, shadows, partial facial visibility, camera orientation issues, and lower recognition rates.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Real-Time Facial Expression Recognition Using Deep Learning

Haq,

Akram,

Irshad

et al. 2024

ATAIML

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In the realm of facial expression recognition (FER), the identification and classification of seven universal emotional states, surprise, disgust, fear, happiness, neutrality, anger, and contempt, are of paramount importance. This research focuses on the application of convolutional neural networks (CNNs) for the extraction and categorization of these expressions. Over the past decade, CNNs have emerged as a significant area of research in human-computer interaction, surpassing previous methodologies with their superior feature learning capabilities. While current models demonstrate exceptional accuracy in recognizing facial expressions within controlled laboratory datasets, their performance significantly diminishes when applied to real-time, uncontrolled datasets. Challenges such as degraded image quality, occlusions, variable lighting, and alterations in head pose are commonly encountered in images sourced from unstructured environments like the internet. This study aims to enhance the recognition accuracy of FER by employing deep learning techniques to process images captured in real-time, particularly those of lower resolution. The objective is to augment the accuracy of FER in real-world datasets, which are inherently more complex and collected under less controlled conditions, compared to laboratory-collected data. The effectiveness of a deep learning-based approach to emotion detection in photographs is rigorously evaluated in this work. The proposed method is exhaustively compared with manual techniques and other existing approaches to assess its efficacy. This comparison forms the foundation for a subjective evaluation methodology, focusing on validation and end-user satisfaction. The findings conclusively demonstrate the method's proficiency in accurately recognizing emotions in both laboratory and real-world scenarios, thereby underscoring the potential of deep learning in the domain of facial emotion identification.

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Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Cited by 8 publications

References 21 publications

Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network

Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network

Interplay of Government Support and Foreign Investment in Enhancing R&D in China’s Strategic Sectors

Enhanced Real-Time Facial Expression Recognition Using Deep Learning

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