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MediaPipe</title> <meta name="description" content="Search results for: MediaPipe"> <meta name="keywords" content="MediaPipe"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="MediaPipe"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: MediaPipe</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> Fitness Action Recognition Based on MediaPipe</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zixuan%20Xu">Zixuan Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yichun%20Lou"> Yichun Lou</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Song"> Yang Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Zihuai%20Lin"> Zihuai Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=MediaPipe" title=" MediaPipe"> MediaPipe</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20boosting" title=" adaptive boosting"> adaptive boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20dynamic%20time%20warping" title=" fast dynamic time warping"> fast dynamic time warping</a> </p> <a href="https://publications.waset.org/abstracts/160758/fitness-action-recognition-based-on-mediapipe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Real-Time Finger Tracking: Evaluating YOLOv8 and MediaPipe for Enhanced HCI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Alipour">Zahra Alipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Amirreza%20Moheb%20Afzali"> Amirreza Moheb Afzali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of human-computer interaction (HCI), hand gestures play a crucial role in facilitating communication by expressing emotions and intentions. The precise tracking of the index finger and the estimation of joint positions are essential for developing effective gesture recognition systems. However, various challenges, such as anatomical variations, occlusions, and environmental influences, hinder optimal functionality. This study investigates the performance of the YOLOv8m model for hand detection using the EgoHands dataset, which comprises diverse hand gesture images captured in various environments. Over three training processes, the model demonstrated significant improvements in precision (from 88.8% to 96.1%) and recall (from 83.5% to 93.5%), achieving a mean average precision (mAP) of 97.3% at an IoU threshold of 0.7. We also compared YOLOv8m with MediaPipe and an integrated YOLOv8 + MediaPipe approach. The combined method outperformed the individual models, achieving an accuracy of 99% and a recall of 99%. These findings underscore the benefits of model integration in enhancing gesture recognition accuracy and localization for real-time applications. The results suggest promising avenues for future research in HCI, particularly in augmented reality and assistive technologies, where improved gesture recognition can significantly enhance user experience. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=YOLOv8" title="YOLOv8">YOLOv8</a>, <a href="https://publications.waset.org/abstracts/search?q=mediapipe" title=" mediapipe"> mediapipe</a>, <a href="https://publications.waset.org/abstracts/search?q=finger%20tracking" title=" finger tracking"> finger tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20estimation" title=" joint estimation"> joint estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=human-computer%20interaction%20%28HCI%29" title=" human-computer interaction (HCI)"> human-computer interaction (HCI)</a> </p> <a href="https://publications.waset.org/abstracts/194650/real-time-finger-tracking-evaluating-yolov8-and-mediapipe-for-enhanced-hci" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194650.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">5</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Real-Time Fitness Monitoring with MediaPipe</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Prayaga">Chandra Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Lakshmi%20Prayaga"> Lakshmi Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Aaron%20Wade"> Aaron Wade</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyle%20Rank"> Kyle Rank</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopi%20Shankar%20Mallu"> Gopi Shankar Mallu</a>, <a href="https://publications.waset.org/abstracts/search?q=Sri%20Satya"> Sri Satya</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsha%20Pola"> Harsha Pola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's tech-driven world, where connectivity shapes our daily lives, maintaining physical and emotional health is crucial. Athletic trainers play a vital role in optimizing athletes' performance and preventing injuries. However, a shortage of trainers impacts the quality of care. This study introduces a vision-based exercise monitoring system leveraging Google's MediaPipe library for precise tracking of bicep curl exercises and simultaneous posture monitoring. We propose a three-stage methodology: landmark detection, side detection, and angle computation. Our system calculates angles at the elbow, wrist, neck, and torso to assess exercise form. Experimental results demonstrate the system's effectiveness in distinguishing between good and partial repetitions and evaluating body posture during exercises, providing real-time feedback for precise fitness monitoring. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=physical%20health" title="physical health">physical health</a>, <a href="https://publications.waset.org/abstracts/search?q=athletic%20trainers" title=" athletic trainers"> athletic trainers</a>, <a href="https://publications.waset.org/abstracts/search?q=fitness%20monitoring" title=" fitness monitoring"> fitness monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=technology%20driven%20solutions" title=" technology driven solutions"> technology driven solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%E2%80%99s%20MediaPipe" title=" Google鈥檚 MediaPipe"> Google鈥檚 MediaPipe</a>, <a href="https://publications.waset.org/abstracts/search?q=landmark%20detection" title=" landmark detection"> landmark detection</a>, <a href="https://publications.waset.org/abstracts/search?q=angle%20computation" title=" angle computation"> angle computation</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20feedback" title=" real-time feedback"> real-time feedback</a> </p> <a href="https://publications.waset.org/abstracts/183020/real-time-fitness-monitoring-with-mediapipe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183020.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">66</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Hands-off Parking: Deep Learning Gesture-based System for Individuals with Mobility Needs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Javier%20Romera">Javier Romera</a>, <a href="https://publications.waset.org/abstracts/search?q=Alberto%20Justo"> Alberto Justo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ignacio%20Fidalgo"> Ignacio Fidalgo</a>, <a href="https://publications.waset.org/abstracts/search?q=Joshue%20Perez"> Joshue Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Javier%20Araluce"> Javier Araluce</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, individuals with mobility needs face a significant challenge when docking vehicles. In many cases, after parking, they encounter insufficient space to exit, leading to two undesired outcomes: either avoiding parking in that spot or settling for improperly placed vehicles. To address this issue, the following paper presents a parking control system employing gestural teleoperation. The system comprises three main phases: capturing body markers, interpreting gestures, and transmitting orders to the vehicle. The initial phase is centered around the MediaPipe framework, a versatile tool optimized for real-time gesture recognition. MediaPipe excels at detecting and tracing body markers, with a special emphasis on hand gestures. Hands detection is done by generating 21 reference points for each hand. Subsequently, after data capture, the project employs the MultiPerceptron Layer (MPL) for indepth gesture classification. This tandem of MediaPipe's extraction prowess and MPL's analytical capability ensures that human gestures are translated into actionable commands with high precision. Furthermore, the system has been trained and validated within a built-in dataset. To prove the domain adaptation, a framework based on the Robot Operating System (ROS), as a communication backbone, alongside CARLA Simulator, is used. Following successful simulations, the system is transitioned to a real-world platform, marking a significant milestone in the project. This real vehicle implementation verifies the practicality and efficiency of the system beyond theoretical constructs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gesture%20detection" title="gesture detection">gesture detection</a>, <a href="https://publications.waset.org/abstracts/search?q=mediapipe" title=" mediapipe"> mediapipe</a>, <a href="https://publications.waset.org/abstracts/search?q=multiperceptron%20layer" title=" multiperceptron layer"> multiperceptron layer</a>, <a href="https://publications.waset.org/abstracts/search?q=robot%20operating%20system" title=" robot operating system"> robot operating system</a> </p> <a href="https://publications.waset.org/abstracts/174862/hands-off-parking-deep-learning-gesture-based-system-for-individuals-with-mobility-needs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174862.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> A Unified Webcam Proctoring Solution on Edge</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saw%20Thiha">Saw Thiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Jay%20Rajasekera"> Jay Rajasekera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A boom in video conferencing generated millions of hours of video data daily to be analyzed. However, such enormous data pose certain scalability issues to be analyzed efficiently, let alone do it in real-time, as online conferences can involve hundreds of people and can last for hours. This paper proposes an efficient online proctoring solution that can analyze the online conferences real-time on edge devices such as Android, iOS, and desktops. Since the computation can be done upfront on the devices where online conferences take place, it can scale well without requiring intensive resources such as GPU servers and complex cloud infrastructure. According to the linear models, face orientation does indeed impact the perceived eye openness. Also, the proposed z score facial landmark standardization was proven to be functional in detecting face orientation and contributed to classifying eye blinks with single eyelid distance computation while achieving a better f1 score and accuracy than the Eye Aspect Ratio (EAR) threshold method. Last but not least, the authors implemented the solution natively in the MediaPipe framework and open-sourced it along with the reproducible experimental results on GitHub. The solution provides face orientation, eye blink, facial activity, and translation detections out of the box and is highly customizable and extensible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=android" title="android">android</a>, <a href="https://publications.waset.org/abstracts/search?q=desktop" title=" desktop"> desktop</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20computing" title=" edge computing"> edge computing</a>, <a href="https://publications.waset.org/abstracts/search?q=blink" title=" blink"> blink</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20orientation" title=" face orientation"> face orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20activity%20and%20translation" title=" facial activity and translation"> facial activity and translation</a>, <a href="https://publications.waset.org/abstracts/search?q=MediaPipe" title=" MediaPipe"> MediaPipe</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source" title=" open source"> open source</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time" title=" real-time"> real-time</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20conference" title=" video conference"> video conference</a>, <a href="https://publications.waset.org/abstracts/search?q=web" title=" web"> web</a>, <a href="https://publications.waset.org/abstracts/search?q=iOS" title=" iOS"> iOS</a>, <a href="https://publications.waset.org/abstracts/search?q=Z%20score%20facial%20landmark%20standardization" title=" Z score facial landmark standardization"> Z score facial landmark standardization</a> </p> <a href="https://publications.waset.org/abstracts/155052/a-unified-webcam-proctoring-solution-on-edge" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155052.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Gesture-Controlled Interface Using Computer Vision and Python</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vedant%20Vardhan%20Rathour">Vedant Vardhan Rathour</a>, <a href="https://publications.waset.org/abstracts/search?q=Anant%20Agrawal"> Anant Agrawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computer using hand gestures and voice commands. The system leverages advanced computer vision techniques using the MediaPipe framework and OpenCV to detect and interpret real time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the Speech Recognition library allows for seamless execution of tasks like web searches, location navigation and gesture control on the system through voice commands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title="gesture recognition">gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20tracking" title=" hand tracking"> hand tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/193844/gesture-controlled-interface-using-computer-vision-and-python" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193844.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">12</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> A Machine Learning Pipeline for Real-Time Activity Detection on Low Computational Power Devices for Metaverse Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kumar">Amit Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Amanpreet%20Chander"> Amanpreet Chander</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Sahani"> Ashish Sahani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents our recent work on real-time human activity detection based on the media pipe pipeline and machine learning algorithms. The proposed system can detect human activities, including running, jumping, squatting, bending to the left or right, and standing still. This is a robust solution for developing a yoga, dance, metaverse, and fitness application that checks for the correction of the pose without having any additional monitor like a personal trainer. MediaPipe solution offers an open-source cross-platform which utilizes a two-step detector-tracker ML pipeline for live detection of key landmarks on our body which can be used for motion data collection. The prediction of real-time poses uses a variety of machine learning techniques and different types of analysis. Without primarily relying on powerful desktop environments for inference, our method achieves real-time performance on the majority of contemporary mobile phones, desktops/laptops, Python, or even the web. Experimental results show that our method outperforms the existing method in terms of accuracy and real-time capability, achieving an accuracy of 99.92% on testing datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20detection" title="human activity detection">human activity detection</a>, <a href="https://publications.waset.org/abstracts/search?q=media%20pipe" title=" media pipe"> media pipe</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=metaverse%20applications" title=" metaverse applications"> metaverse applications</a> </p> <a href="https://publications.waset.org/abstracts/155965/a-machine-learning-pipeline-for-real-time-activity-detection-on-low-computational-power-devices-for-metaverse-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155965.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">179</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Image Processing techniques for Surveillance in Outdoor Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jayanth%20C.">Jayanth C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Anirudh%20Sai%20Yetikuri"> Anirudh Sai Yetikuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Kavitha%20S.%20N."> Kavitha S. N.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20detection" title=" pose detection"> pose detection</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20recognition" title=" facial recognition"> facial recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20plate%20extraction" title=" number plate extraction"> number plate extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20analysis" title=" real-time analysis"> real-time analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=database%20management" title=" database management"> database management</a> </p> <a href="https://publications.waset.org/abstracts/191153/image-processing-techniques-for-surveillance-in-outdoor-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191153.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">26</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a 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