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An object detection model may be trained to recognise and detect several objects, making it adaptable. Object detection had a large scope of interest even before deep learning approaches and modern-day image processing capabilities. It has become considerably more widespread in the present generation, thanks to the development of convolutional neural networks (CNNs) and the adaptation of computer vision technology. 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An object detection model may be trained to recognise and detect several objects, making it adaptable. Object detection had a large scope of interest even before deep learning approaches and modern-day image processing capabilities. It has become considerably more widespread in the present generation, thanks to the development of convolutional neural networks (CNNs) and the adaptation of computer vision technology. The latest wave of deep learning approaches to object detection gives up apparently limitless possibilities.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":84604028,"attachmentType":"pdf","workUrl":"https://www.academia.edu/77160340/REVIEW_ON_OBJECT_DETECTION_WITH_CNN"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":84604028,"attachmentType":"pdf","workUrl":"https://www.academia.edu/77160340/REVIEW_ON_OBJECT_DETECTION_WITH_CNN"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="84604028" data-landing_url="https://www.academia.edu/77160340/REVIEW_ON_OBJECT_DETECTION_WITH_CNN" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="88375158" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/88375158/A_Review_of_Object_Detection_Models_Based_on_Convolutional_Neural_Network">A Review of Object Detection Models Based on Convolutional Neural Network</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="103141834" href="https://buet.academia.edu/ABUSUFIAN">ABU SUFIAN</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Advances in Intelligent Systems and Computing, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Review of Object Detection Models Based on Convolutional Neural Network","attachmentId":92360052,"attachmentType":"pdf","work_url":"https://www.academia.edu/88375158/A_Review_of_Object_Detection_Models_Based_on_Convolutional_Neural_Network","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/88375158/A_Review_of_Object_Detection_Models_Based_on_Convolutional_Neural_Network"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="43726642" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43726642/Object_Detection_using_Deep_Learning_Algorithm_CNN">Object Detection using Deep Learning Algorithm CNN</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Object Detection using Deep Learning Algorithm CNN","attachmentId":64035558,"attachmentType":"pdf","work_url":"https://www.academia.edu/43726642/Object_Detection_using_Deep_Learning_Algorithm_CNN","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/43726642/Object_Detection_using_Deep_Learning_Algorithm_CNN"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="41617707" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/41617707/Object_Detection_with_Deep_Learning_A_Review">Object Detection with Deep Learning: A Review</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="141899839" href="https://independent.academia.edu/yueloufang">yuelou fang</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Object Detection with Deep Learning: A Review","attachmentId":61773718,"attachmentType":"pdf","work_url":"https://www.academia.edu/41617707/Object_Detection_with_Deep_Learning_A_Review","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/41617707/Object_Detection_with_Deep_Learning_A_Review"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="42798727" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/42798727/IEEE_TRANSACTIONS_ON_NEURAL_NETWORKS_AND_LEARNING_SYSTEMS_1_Object_Detection_With_Deep_Learning_A_Review">IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Object Detection With Deep Learning: A Review</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="154860094" href="https://vnuhcm.academia.edu/V%C3%B5Ho%C3%A0ngTh%E1%BB%A7yTi%C3%AAn">Võ Hoàng Thủy Tiên</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Object Detection With Deep Learning: A Review","attachmentId":63026559,"attachmentType":"pdf","work_url":"https://www.academia.edu/42798727/IEEE_TRANSACTIONS_ON_NEURAL_NETWORKS_AND_LEARNING_SYSTEMS_1_Object_Detection_With_Deep_Learning_A_Review","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/42798727/IEEE_TRANSACTIONS_ON_NEURAL_NETWORKS_AND_LEARNING_SYSTEMS_1_Object_Detection_With_Deep_Learning_A_Review"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="63506075" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/63506075/An_Overview_of_Deep_Learning_Based_Object_Detection_Methods">An Overview of Deep Learning-Based Object Detection Methods</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="136502108" href="https://univbatna.academia.edu/LarbiGuezouli">Larbi Guezouli</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><p class="ds-related-work--abstract ds2-5-body-sm">In recent years, there has been rapid development in the research area of deep learning. Deep learning was used to solve different problems, such as visual recognition, speech recognition and handwriting recognition and was achieved a very good performance. In deep learning, Convolutional Neural Networks (ConvNets or CNNs) are found to give the most accurate results, in solving object detection problems. In this paper we&#39;ll go into summarizing some of the most important deep learning models used for object detection tasks over this last recent year, since the creation of AlexNet in 2012. Then, we&#39;ll make a comparison in terms of speed and accuracy between the most used state-of-the-art methods in object detection. Keywords— Object Detection, Deep Learning Methods, Convolutional Neural Networks</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Overview of Deep Learning-Based Object Detection Methods","attachmentId":75916085,"attachmentType":"pdf","work_url":"https://www.academia.edu/63506075/An_Overview_of_Deep_Learning_Based_Object_Detection_Methods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/63506075/An_Overview_of_Deep_Learning_Based_Object_Detection_Methods"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="67763862" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/67763862/Object_Detection_Using_Deep_Learning">Object Detection Using Deep Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="194931094" href="https://independent.academia.edu/MukulBhardwaj29">Mukul Bhardwaj</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Object Detection Using Deep Learning","attachmentId":78473879,"attachmentType":"pdf","work_url":"https://www.academia.edu/67763862/Object_Detection_Using_Deep_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/67763862/Object_Detection_Using_Deep_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="79688710" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79688710/Deep_Learning_Object_Detector_Using_a_Combination_of_Convolutional_Neural_Network_CNN_Architecture_MiniVGGNet_and_Classic_Object_Detection_Algorithm">Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="10246969" href="https://independent.academia.edu/azurachesoh">azura che soh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Pertanika Journal of Science and Technology, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding wind...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm","attachmentId":86317614,"attachmentType":"pdf","work_url":"https://www.academia.edu/79688710/Deep_Learning_Object_Detector_Using_a_Combination_of_Convolutional_Neural_Network_CNN_Architecture_MiniVGGNet_and_Classic_Object_Detection_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79688710/Deep_Learning_Object_Detector_Using_a_Combination_of_Convolutional_Neural_Network_CNN_Architecture_MiniVGGNet_and_Classic_Object_Detection_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="103136431" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/103136431/Deep_Learning_Approaches_for_Detecting_Objects_from_Images_A_Review">Deep Learning Approaches for Detecting Objects from Images: A Review</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="285265" href="https://iiita.academia.edu/SiddharthRautaray">Siddharth Rautaray</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Advances in Intelligent Systems and Computing, 2018</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Deep Learning Approaches for Detecting Objects from Images: A Review","attachmentId":103223757,"attachmentType":"pdf","work_url":"https://www.academia.edu/103136431/Deep_Learning_Approaches_for_Detecting_Objects_from_Images_A_Review","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/103136431/Deep_Learning_Approaches_for_Detecting_Objects_from_Images_A_Review"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="89238819" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89238819/Real_Time_Object_Detection_using_CNN">Real Time Object Detection using CNN</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="242195637" href="https://independent.academia.edu/AkashTripathi96">Akash Tripathi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering &amp; Technology, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obtain high accuracy output with small sample data to train the model by integrating the CNN and SIFT features. The proposed detection model is a cluster of multiple deep convolutional neural networks and hybrid CNN-SIFT algorithm. The reason to use the SIFT featureis to amplify the model‟s capacity to detect small data or features as the SIFT requires small datasets to detect objects. Our simulati...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Real Time Object Detection using CNN","attachmentId":93074247,"attachmentType":"pdf","work_url":"https://www.academia.edu/89238819/Real_Time_Object_Detection_using_CNN","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/89238819/Real_Time_Object_Detection_using_CNN"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="64673699" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/64673699/Deep_Learning_Approaches_for_Object_Detection">Deep Learning Approaches for Object Detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6266202" href="https://independent.academia.edu/sushmajaiswal">sushma jaiswal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Deep Learning Approaches for Object Detection","attachmentId":76601168,"attachmentType":"pdf","work_url":"https://www.academia.edu/64673699/Deep_Learning_Approaches_for_Object_Detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/64673699/Deep_Learning_Approaches_for_Object_Detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":84604028,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":84604028,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_84604028" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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