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 10 publications

(9 citation statements)

References 21 publications

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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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“…This work exemplifies the application of machine learning in media analysis, offering a novel perspective in the field. In a distinct context, Haq and Saqlain [23] responded to the challenges posed by the COVID-19 pandemic by developing an iris detection system for monitoring attendance in educational institutes. Their application of machine learning presents a pragmatic approach to verifying student presence during the pandemic.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Misinformation Control: A Cloud-Enhanced Machine Learning Approach

Baig,

Akram,

Haq

et al. 2024

IDA

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The digital age has witnessed the rampant spread of misinformation, significantly impacting the medical and financial sectors. This phenomenon, fueled by various sources, contributes to public distress and information warfare, necessitating robust countermeasures. In response, a novel model has been developed, integrating cloud computing with advanced machine learning techniques. This model prioritizes the identification and mitigation of false information through optimized classification strategies. Utilizing diverse datasets for predictive analysis, the model employs state-of-the-art algorithms, including K-Nearest Neighbors (KNN) and Random Forest (RF), to enhance accuracy and efficiency. A distinctive feature of this approach is the implementation of cloud-empowered transfer learning, providing a scalable and optimized solution to address the challenges posed by the vast, yet often unreliable, information available online. By harnessing the potential of cloud computing and machine learning, this model offers a strategic approach to combating the prevalent issue of misinformation in the digital world.

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

Cited by 10 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

Optimizing Misinformation Control: A Cloud-Enhanced Machine Learning Approach

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