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learning apps</title> <meta name="description" content="Search results for: deep learning apps"> <meta name="keywords" content="deep learning apps"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="deep learning apps"> <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> 8544</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: deep learning apps</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8544</span> DLtrace: Toward Understanding and Testing Deep Learning Information Flow in Deep Learning-Based Android Apps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jie%20Zhang">Jie Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qianyu%20Guo"> Qianyu Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Tieyi%20Zhang"> Tieyi Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiyong%20Feng"> Zhiyong Feng</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaohong%20Li"> Xiaohong Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20computing" title="mobile computing">mobile computing</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20apps" title=" deep learning apps"> deep learning apps</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitive%20information" title=" sensitive information"> sensitive information</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20analysis" title=" static analysis"> static analysis</a> </p> <a href="https://publications.waset.org/abstracts/152909/dltrace-toward-understanding-and-testing-deep-learning-information-flow-in-deep-learning-based-android-apps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152909.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">177</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">8543</span> On a Theoretical Framework for Language Learning Apps Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juan%20Manuel%20Real-Espinosa">Juan Manuel Real-Espinosa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the first step to evaluate language learning apps: what theoretical framework to adopt when designing the app evaluation framework. The answer is not just one since there are several options that could be proposed. However, the question to be clarified is to what extent the learning design of apps is based on a specific learning approach, or on the contrary, on a fusion of elements from several theoretical proposals and paradigms, such as m-learning, mobile assisted language learning, and a number of theories about language acquisition. The present study suggests that the reality is closer to the second assumption. This implies that the theoretical framework against which the learning design of the apps should be evaluated must also be a hybrid theoretical framework, which integrates evaluation criteria from the different theories involved in language learning through mobile applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile-assisted%20language%20learning" title="mobile-assisted language learning">mobile-assisted language learning</a>, <a href="https://publications.waset.org/abstracts/search?q=action-oriented%20approach" title=" action-oriented approach"> action-oriented approach</a>, <a href="https://publications.waset.org/abstracts/search?q=apps%20evaluation" title=" apps evaluation"> apps evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=post-method%20pedagogy" title=" post-method pedagogy"> post-method pedagogy</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20language%20acquisition" title=" second language acquisition"> second language acquisition</a> </p> <a href="https://publications.waset.org/abstracts/144748/on-a-theoretical-framework-for-language-learning-apps-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144748.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">206</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">8542</span> Blocking of Random Chat Apps at Home Routers for Juvenile Protection in South Korea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Jin%20Kwon">Min Jin Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung%20Won%20Kim"> Seung Won Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Eui%20Yeon%20Kim"> Eui Yeon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Haeyoung%20Lee"> Haeyoung Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous anonymous chat apps that help people to connect with random strangers have been released in South Korea. However, they become a serious problem for young people since young people often use them for channels of prostitution or sexual violence. Although ISPs in South Korea are responsible for making inappropriate content inaccessible on their networks, they do not block traffic of random chat apps since 1) the use of random chat apps is entirely legal. 2) it is reported that they use HTTP proxy blocking so that non-HTTP traffic cannot be blocked. In this paper, we propose a service model that can block random chat apps at home routers. A service provider manages a blacklist that contains blocked apps’ information. Home routers that subscribe the service filter the traffic of the apps out using deep packet inspection. We have implemented a prototype of the proposed model, including a centralized server providing the blacklist, a Raspberry Pi-based home router that can filter traffic of the apps out, and an Android app used by the router’s administrator to locally customize the blacklist. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20packet%20inspection" title="deep packet inspection">deep packet inspection</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20filtering" title=" internet filtering"> internet filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=juvenile%20protection" title=" juvenile protection"> juvenile protection</a>, <a href="https://publications.waset.org/abstracts/search?q=technical%20blocking" title=" technical blocking"> technical blocking</a> </p> <a href="https://publications.waset.org/abstracts/66686/blocking-of-random-chat-apps-at-home-routers-for-juvenile-protection-in-south-korea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66686.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">349</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">8541</span> Classification Based on Deep Neural Cellular Automata Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasser%20F.%20Hassan">Yasser F. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20cellular%20automata" title=" neural cellular automata"> neural cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/104722/classification-based-on-deep-neural-cellular-automata-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104722.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">198</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">8540</span> Communicative Competence in French Language for Nigerian Teacher-Trainees in the New-Normal Society Using Mobile Apps as a Lifelong Learning Tool</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olukemi%20E.%20Adetuyi-Olu-Francis">Olukemi E. Adetuyi-Olu-Francis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Learning is natural for living. One stops learning when life ends. Hence, there is no negotiating life-long learning. An individual has the innate ability to learn as many languages as he/she desires as long as life exists. French language education to every Nigerian teacher-trainee is a necessity. Nigeria’s geographical location requires that the French language should be upheld for economic and cultural co-operations between Nigeria and the francophone countries sharing borders with her. The French language will enhance the leadership roles of the teacher-trainees and their ability to function across borders. The 21st century learning tools are basically digital, and many apps are complementing the actual classroom interactions. This study examined the communicative competence in the French language to equip Nigerian teacher-trainees in the new-normal society using mobile apps as a lifelong learning tool. Three research questions and hypotheses guided the study, and the researcher adopted a pre-test, a post-test experimental design, using a sample size of 87 teacher-trainees in South-south geopolitical zone of Nigeria. Results showed that the use of mobile apps is effective for learning the French language. One of the recommendations is that the use of mobile apps should be encouraged for all Nigerian youths to learn the French language for enhancing leadership roles in the world of work and for international interactions for socio-economic co-operations with Nigerian neighboring countries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communicative%20competence" title="communicative competence">communicative competence</a>, <a href="https://publications.waset.org/abstracts/search?q=french%20language" title=" french language"> french language</a>, <a href="https://publications.waset.org/abstracts/search?q=life%20long%20learning" title=" life long learning"> life long learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20apps" title=" mobile apps"> mobile apps</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20normal%20society" title=" new normal society"> new normal society</a>, <a href="https://publications.waset.org/abstracts/search?q=teacher%20trainees" title=" teacher trainees"> teacher trainees</a> </p> <a href="https://publications.waset.org/abstracts/139606/communicative-competence-in-french-language-for-nigerian-teacher-trainees-in-the-new-normal-society-using-mobile-apps-as-a-lifelong-learning-tool" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139606.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">235</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">8539</span> B4A Is One of the Best Programming Software for Surveyor Engineers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Mohammadi">Ali Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many engineers use the programs that are installed on the computer, but with the arrival of the mobile phone and the possibility of designing apps, many Android programs can be designed similar to the programs that are installed on the computer, and from the mobile phone, in addition to communication Telephone and photography show a more practical use. Engineers are one of the groups that can use specialized apps to have less need to go to the office and computer, and b4a can be considered one of the simplest software for designing apps. This article introduces a number of surveying apps designed using b4a and the impact that using these apps has on productivity in this field of engineering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=app" title="app">app</a>, <a href="https://publications.waset.org/abstracts/search?q=tunnel" title=" tunnel"> tunnel</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20station" title=" total station"> total station</a>, <a href="https://publications.waset.org/abstracts/search?q=map" title=" map"> map</a> </p> <a href="https://publications.waset.org/abstracts/184214/b4a-is-one-of-the-best-programming-software-for-surveyor-engineers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184214.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">48</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">8538</span> A Deep Learning Approach to Subsection Identification in Electronic Health Records</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nitin%20Shravan">Nitin Shravan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudarsun%20Santhiappan"> Sudarsun Santhiappan</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Sivaselvan"> B. Sivaselvan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</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=semantic%20clinical%20classification" title=" semantic clinical classification"> semantic clinical classification</a>, <a href="https://publications.waset.org/abstracts/search?q=subsection%20identification" title=" subsection identification"> subsection identification</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a> </p> <a href="https://publications.waset.org/abstracts/109176/a-deep-learning-approach-to-subsection-identification-in-electronic-health-records" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109176.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">217</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">8537</span> A Review of Machine Learning for Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aravindraj%20D."> Aravindraj D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadathulla%20A."> Sadathulla A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title="active learning">active learning</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/72161/a-review-of-machine-learning-for-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72161.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">445</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">8536</span> A Comparative Study of Deep Learning Methods for COVID-19 Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aishrith%20Rao">Aishrith Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <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=radiology" title=" radiology"> radiology</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG-19" title=" VGG-19"> VGG-19</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title=" deep neural networks"> deep neural networks</a> </p> <a href="https://publications.waset.org/abstracts/127887/a-comparative-study-of-deep-learning-methods-for-covid-19-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127887.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">160</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">8535</span> Collaborative Writing on Line with Apps During the Time of Pandemic: A Systematic Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giuseppe%20Liverano">Giuseppe Liverano</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today’s school iscalledupon to take the lead role in supporting students towards the formation of conscious identity and a sense of responsible citizenship, through the development of key competencies for lifelong learning A rolethatrequiresit to be ready for change and to respond to the ever new needs of students, by adopting new pedagogical and didactic models and new didactic devices. Information and Communication Technologies, in this sense, reveal themselves to be usefulresourcesthatpermit to focus attention on the learning of eachindividualstudentunderstoodas a dynamic and relational process of constructing shared and participatedmeanings. The use of collaborative writing apps represents a democratic and shared knowledge way of constructionthroughICTs. It promotes the learning of reading-writing, literacy, and the development of transversal competencies in an inclusive perspective peer-to-peer comparison and reflectionthatstimulates the transfer of thought into speech and writing, the transformation of knowledge through a trialogicalapproach to learning generates enthusiasm and strengthensmotivationItrepresents a “different” way of expressing the training needs which come from several disciplinary fields of subjects with different cultures. The contribution aims to reflect on the formative value of collaborative writing through apps and analyse some proposals on line at school during the time of pandemic in order to highlight their critical aspects and pedagogical perspectives. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collaborative%20writing" title="collaborative writing">collaborative writing</a>, <a href="https://publications.waset.org/abstracts/search?q=formative%20value" title=" formative value"> formative value</a>, <a href="https://publications.waset.org/abstracts/search?q=online" title=" online"> online</a>, <a href="https://publications.waset.org/abstracts/search?q=apps" title=" apps"> apps</a>, <a href="https://publications.waset.org/abstracts/search?q=pandemic" title=" pandemic"> pandemic</a> </p> <a href="https://publications.waset.org/abstracts/143467/collaborative-writing-on-line-with-apps-during-the-time-of-pandemic-a-systematic-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143467.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">157</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">8534</span> Performance Evaluation of Distributed Deep Learning Frameworks in Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuen-Tai%20Wang">Shuen-Tai Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang-An%20Kuo"> Fang-An Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chau-Yi%20Chou"> Chau-Yi Chou</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Bin%20Fang"> Yu-Bin Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</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=deep%20learning" title=" deep learning"> deep 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/110135/performance-evaluation-of-distributed-deep-learning-frameworks-in-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110135.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">211</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">8533</span> Apps Reduce the Cost of Construction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Mohammadi">Ali Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Every construction that is done, the most important part of attention for employers and contractors is its cost, and they always try to reduce costs so that they can compete in the market, so they estimate the cost of construction before starting their activities. The costs can be generally divided into four parts: the materials used, the equipment used, the manpower required, and the time required. In this article, we are trying to talk about the three items of equipment, manpower, and time, and examine how the use of apps can reduce the cost of construction, while due to various reasons, it has received less attention in the field of app design. Also, because we intend to use these apps in construction and they are used by engineers and experts, we define these apps as engineering apps because the idea of their design must be by an engineer who works in that field. Also, considering that most engineers are familiar with programming during their studies, they can design the apps they need using simple programming software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=layout" title="layout">layout</a>, <a href="https://publications.waset.org/abstracts/search?q=as-bilt" title=" as-bilt"> as-bilt</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=maps" title=" maps"> maps</a> </p> <a href="https://publications.waset.org/abstracts/183643/apps-reduce-the-cost-of-construction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183643.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">65</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">8532</span> Facial Emotion Recognition Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Mishra">Ashutosh Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Goyal"> Nikhil Goyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations. <p class="card-text"><strong>Keywords:</strong> <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=computational%20intelligence" title=" computational intelligence"> computational intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20map" title=" depth map"> depth map</a> </p> <a href="https://publications.waset.org/abstracts/139253/facial-emotion-recognition-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139253.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">231</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">8531</span> Bridging Consumer Farmer Mobile Application Divide</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20Hol">Ana Hol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technological inventions such as websites, blogs, smartphone applications are on a daily basis influencing our decision making, are improving our productivity and are shaping futures of many consumer and service/product providers. This research identifies that these days both customers and providers heavily rely on smart phone applications. With this in mind, iTunes mobile applications store has been studies. It was identified that food related applications used by consumers can broadly be categorized into purchase apps, diaries, tracking health apps, trip farm location apps and cooking apps. On the other hand, apps used by farmers can be classified as: weather apps, pests / fertilizer app and general Facebook apps. With the aim to blur this farmer-consumer divide our research utilizes Context Specific eTransformation Framework and based on it identifies characteristic of the app that would allow this to happen. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smart%20phone%20applications" title="smart phone applications">smart phone applications</a>, <a href="https://publications.waset.org/abstracts/search?q=SME%20-%20farmers" title=" SME - farmers"> SME - farmers</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer" title=" consumer"> consumer</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology"> technology</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20innovation" title=" business innovation"> business innovation</a> </p> <a href="https://publications.waset.org/abstracts/33111/bridging-consumer-farmer-mobile-application-divide" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33111.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">383</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">8530</span> How to Guide Students from Surface to Deep Learning: Applied Philosophy in Management Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lihong%20Wu">Lihong Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Young"> Raymond Young</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ability to learn is one of the most critical skills in the information age. However, many students do not have a clear understanding of what learning is, what they are learning, and why they are learning. Many students study simply to pass rather than to learn something useful for their career and their life. They have a misconception about learning and a wrong attitude towards learning. This research explores student attitudes to study in management education and explores how to intercede to lead students from shallow to deeper modes of learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge" title="knowledge">knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20learning" title=" surface learning"> surface learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a> </p> <a href="https://publications.waset.org/abstracts/143479/how-to-guide-students-from-surface-to-deep-learning-applied-philosophy-in-management-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143479.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">501</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">8529</span> A Deep Reinforcement Learning-Based Secure Framework against Adversarial Attacks in Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arshia%20Aflaki">Arshia Aflaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadis%20Karimipour"> Hadis Karimipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Anik%20Islam"> Anik Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20attack" title="generative adversarial attack">generative adversarial attack</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=IIoT" title=" IIoT"> IIoT</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system" title=" power system"> power system</a> </p> <a href="https://publications.waset.org/abstracts/188908/a-deep-reinforcement-learning-based-secure-framework-against-adversarial-attacks-in-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188908.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">36</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">8528</span> A Less Complexity Deep Learning Method for Drones Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Kassab">Mohamad Kassab</a>, <a href="https://publications.waset.org/abstracts/search?q=Amal%20El%20Fallah%20Seghrouchni"> Amal El Fallah Seghrouchni</a>, <a href="https://publications.waset.org/abstracts/search?q=Frederic%20Barbaresco"> Frederic Barbaresco</a>, <a href="https://publications.waset.org/abstracts/search?q=Raed%20Abu%20Zitar"> Raed Abu Zitar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drones%20detection" title="drones detection">drones detection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=birds%20versus%20drones" title=" birds versus drones"> birds versus drones</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20of%20detection" title=" precision of detection"> precision of detection</a>, <a href="https://publications.waset.org/abstracts/search?q=AdderNet" title=" AdderNet"> AdderNet</a> </p> <a href="https://publications.waset.org/abstracts/154403/a-less-complexity-deep-learning-method-for-drones-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154403.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">182</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">8527</span> A Comprehensive Study of Camouflaged Object Detection Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalak%20Bin%20Khair">Khalak Bin Khair</a>, <a href="https://publications.waset.org/abstracts/search?q=Saqib%20Jahir"> Saqib Jahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ibrahim"> Mohammed Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Bin"> Fahad Bin</a>, <a href="https://publications.waset.org/abstracts/search?q=Debajyoti%20Karmaker"> Debajyoti Karmaker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=TensorFlow" title=" TensorFlow"> TensorFlow</a>, <a href="https://publications.waset.org/abstracts/search?q=camouflage" title=" camouflage"> camouflage</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG16" title=" VGG16"> VGG16</a> </p> <a href="https://publications.waset.org/abstracts/152633/a-comprehensive-study-of-camouflaged-object-detection-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152633.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">149</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">8526</span> Computer Science and Mathematics Collaborating to Create New Educational Opportunities While Developing Interactive Calculus Apps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Pargas">R. Pargas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Reba"> M. Reba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since 2006, the School of Computing and the Department of Mathematical Sciences have collaborated on several industry and NSF grants to develop new uses of technology in teaching and learning. Clemson University’s Creative Inquiry Program allowed computer science and mathematics students to earn credit each semester for participating in seminars which introduced them to new areas for independent research. We will discuss how the development of three interactive instructional apps for Calculus resulted not only in a useful product, but also in unique educational benefits for both the computer science students and the mathematics students, graduate and undergraduate, involved in the development process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=calculus" title="calculus">calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=apps" title=" apps"> apps</a>, <a href="https://publications.waset.org/abstracts/search?q=programming" title=" programming"> programming</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics" title=" mathematics"> mathematics</a> </p> <a href="https://publications.waset.org/abstracts/45781/computer-science-and-mathematics-collaborating-to-create-new-educational-opportunities-while-developing-interactive-calculus-apps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45781.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">404</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">8525</span> Adaptive Few-Shot Deep Metric Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wentian%20Shi">Wentian Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Daming%20Shi"> Daming Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maysam%20Orouskhani"> Maysam Orouskhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Tian"> Feng Tian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Whereas currently the most prevalent deep learning methods require a large amount of data for training, few-shot learning tries to learn a model from limited data without extensive retraining. In this paper, we present a loss function based on triplet loss for solving few-shot problem using metric based learning. Instead of setting the margin distance in triplet loss as a constant number empirically, we propose an adaptive margin distance strategy to obtain the appropriate margin distance automatically. We implement the strategy in the deep siamese network for deep metric embedding, by utilizing an optimization approach by penalizing the worst case and rewarding the best. Our experiments on image recognition and co-segmentation model demonstrate that using our proposed triplet loss with adaptive margin distance can significantly improve the performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=few-shot%20learning" title="few-shot learning">few-shot learning</a>, <a href="https://publications.waset.org/abstracts/search?q=triplet%20network" title=" triplet network"> triplet network</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20margin" title=" adaptive margin"> adaptive margin</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/132975/adaptive-few-shot-deep-metric-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132975.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">171</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">8524</span> Breast Cancer Prediction Using Score-Level Fusion of Machine Learning and Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sam%20Khozama">Sam Khozama</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20M.%20Mayya"> Ali M. Mayya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models. <p class="card-text"><strong>Keywords:</strong> <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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20prediction" title=" cancer prediction"> cancer prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a> </p> <a href="https://publications.waset.org/abstracts/155602/breast-cancer-prediction-using-score-level-fusion-of-machine-learning-and-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155602.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">163</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">8523</span> Cyber Attacks Management in IoT Networks Using Deep Learning and Edge Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asmaa%20El%20Harat">Asmaa El Harat</a>, <a href="https://publications.waset.org/abstracts/search?q=Toumi%20Hicham"> Toumi Hicham</a>, <a href="https://publications.waset.org/abstracts/search?q=Youssef%20Baddi"> Youssef Baddi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This survey delves into the complex realm of Internet of Things (IoT) security, highlighting the urgent need for effective cybersecurity measures as IoT devices become increasingly common. It explores a wide array of cyber threats targeting IoT devices and focuses on mitigating these attacks through the combined use of deep learning and machine learning algorithms, as well as edge and cloud computing paradigms. The survey starts with an overview of the IoT landscape and the various types of attacks that IoT devices face. It then reviews key machine learning and deep learning algorithms employed in IoT cybersecurity, providing a detailed comparison to assist in selecting the most suitable algorithms. Finally, the survey provides valuable insights for cybersecurity professionals and researchers aiming to enhance security in the intricate world of IoT. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things%20%28IoT%29" title="internet of things (IoT)">internet of things (IoT)</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</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=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/188872/cyber-attacks-management-in-iot-networks-using-deep-learning-and-edge-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188872.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">31</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">8522</span> Learning on the Go: Practicing Vocabulary with Mobile Apps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shoba%20Bandi-Rao">Shoba Bandi-Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The lack of college readiness is one of the major contributors to low graduation rates at community colleges, especially among educationally and financially disadvantaged students. About 45% of underprepared high school graduates are required to complete ‘remedial’ reading/writing courses before they can begin taking college-level courses. Mobile apps present ‘bite-size’ learning materials that can be useful for practicing certain literacy skills, such as vocabulary learning. The convenience of mobile phones is ideal for a majority of students at community colleges who hold full or part-time jobs. Mobile apps allow students to learn during small ‘chunks’ of time available to them outside of the class—during subway commute, between classes, etc. Learning with mobile apps is a relatively new area in research, and their effectiveness for learning new words has been inconclusive. Using Mishra & Koehler’s TPCK theoretical framework, this study explored the effectiveness of the mobile app (Quizlet) for learning one hundred common college-level words in ‘remedial’ writing class over one semester. Each week, before coming to class, students studied a list of 10-15 words presented in context within sentences. Students came across these words in the article they read in class making their learning more meaningful. A pre and post-test measured the number of words students knew, learned and remembered. Statistical analysis shows that students performed better by 41% on the post-test indicating that the mobile app was helpful for learning words. Students also completed a short survey each week that sought to determine the amount of time students spent on the vocabulary app. A positive correlation was found between the amount of time spent on the mobile app and the number of words learned. The goal of this research is to capitalize on the convenience of smartphones to (1) better prepare them for college-level course work, and (2) contribute to current literature on mobile learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20learning" title="mobile learning">mobile learning</a>, <a href="https://publications.waset.org/abstracts/search?q=vocabulary%20learning" title=" vocabulary learning"> vocabulary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=literacy%20skills" title=" literacy skills"> literacy skills</a>, <a href="https://publications.waset.org/abstracts/search?q=Quizlet" title=" Quizlet"> Quizlet</a> </p> <a href="https://publications.waset.org/abstracts/56352/learning-on-the-go-practicing-vocabulary-with-mobile-apps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56352.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">224</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">8521</span> Deep Reinforcement Learning Model Using Parameterised Quantum Circuits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lokes%20Parvatha%20Kumaran%20S.">Lokes Parvatha Kumaran S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakthi%20Jay%20Mahenthar%20C."> Sakthi Jay Mahenthar C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sathyaprakash%20P."> Sathyaprakash P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayakumar%20V."> Jayakumar V.</a>, <a href="https://publications.waset.org/abstracts/search?q=Shobanadevi%20A."> Shobanadevi A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantum%20computing" title="quantum computing">quantum computing</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20machine%20learning" title=" quantum machine learning"> quantum machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20quantum%20circuit" title=" variational quantum circuit"> variational quantum circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20information%20encoding%20scheme" title=" quantum information encoding scheme"> quantum information encoding scheme</a> </p> <a href="https://publications.waset.org/abstracts/152629/deep-reinforcement-learning-model-using-parameterised-quantum-circuits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152629.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">133</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">8520</span> The Smart Record and Replay Mechanism for Android</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kuei-Chun%20Liu">Kuei-Chun Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Yu%20Lai"> Yu-Yu Lai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Hong%20Wu"> Ching-Hong Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsiao-Han%20Huang"> Hsiao-Han Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The number of Android applications (Apps) has increased rapidly in recent years. In order to get better programmatic control over Apps, we designed a record-and-replay mechanism to record Android input events and accessibility service events then make shortcuts. The shortcut is useful for complicated routine works and to Android beginners. We also generated graphical user interface (GUI) API by these shortcuts. GUI API helps developers make integrated Apps which can control other third-party Apps even if the official API is not offered by their providers. We demonstrated the usage of GUI API with two integrated Apps: Universal Bank App and Universal Communication App. Universal Bank App integrates three accounts from different banks and Universal Communication App integrates Line with WhatsApp. Both of them show the advantage of extendable GUI API. Furthermore, using our mechanism, shortcuts could replay almost all of the Top-100 Apps on Google Play correctly. In sum, the approach we present can help both Android developers and general users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graphical%20user%20interface" title="graphical user interface">graphical user interface</a>, <a href="https://publications.waset.org/abstracts/search?q=GUI%20API" title=" GUI API"> GUI API</a>, <a href="https://publications.waset.org/abstracts/search?q=record-and-replay" title=" record-and-replay"> record-and-replay</a>, <a href="https://publications.waset.org/abstracts/search?q=third-party%20apps" title=" third-party apps"> third-party apps</a> </p> <a href="https://publications.waset.org/abstracts/44813/the-smart-record-and-replay-mechanism-for-android" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44813.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">407</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">8519</span> Deep learning with Noisy Labels : Learning True Labels as Discrete Latent Variable</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azeddine%20El-Hassouny">Azeddine El-Hassouny</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandrashekhar%20Meshram"> Chandrashekhar Meshram</a>, <a href="https://publications.waset.org/abstracts/search?q=Geraldin%20Nanfack"> Geraldin Nanfack</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=label%20noise" title="label noise">label noise</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20latent%20variable" title=" discrete latent variable"> discrete latent variable</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20inference" title=" variational inference"> variational inference</a>, <a href="https://publications.waset.org/abstracts/search?q=MNIST" title=" MNIST"> MNIST</a>, <a href="https://publications.waset.org/abstracts/search?q=CIFAR32" title=" CIFAR32"> CIFAR32</a> </p> <a href="https://publications.waset.org/abstracts/142809/deep-learning-with-noisy-labels-learning-true-labels-as-discrete-latent-variable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142809.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">127</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">8518</span> Deep Learning to Enhance Mathematics Education for Secondary Students in Sri Lanka</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Selvavinayagan%20Babiharan">Selvavinayagan Babiharan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to develop a deep learning platform to enhance mathematics education for secondary students in Sri Lanka. The platform will be designed to incorporate interactive and user-friendly features to engage students in active learning and promote their mathematical skills. The proposed platform will be developed using TensorFlow and Keras, two widely used deep learning frameworks. The system will be trained on a large dataset of math problems, which will be collected from Sri Lankan school curricula. The results of this research will contribute to the improvement of mathematics education in Sri Lanka and provide a valuable tool for teachers to enhance the learning experience of their students. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20technology" title="information technology">information technology</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</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=mathematics" title=" mathematics"> mathematics</a> </p> <a href="https://publications.waset.org/abstracts/166677/deep-learning-to-enhance-mathematics-education-for-secondary-students-in-sri-lanka" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166677.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">83</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">8517</span> Deep Learning for Recommender System: Principles, Methods and Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Basiliyos%20Tilahun%20Betru">Basiliyos Tilahun Betru</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20Awono%20Onana"> Charles Awono Onana</a>, <a href="https://publications.waset.org/abstracts/search?q=Bernabe%20Batchakui"> Bernabe Batchakui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20system" title=" recommender system"> recommender system</a> </p> <a href="https://publications.waset.org/abstracts/74244/deep-learning-for-recommender-system-principles-methods-and-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74244.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">478</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">8516</span> Leveraging Deep Q Networks in Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Liu">Peng Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title="deep reinforcement learning">deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20Q%20networks" title=" deep Q networks"> deep Q networks</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-period%20optimization" title=" multi-period optimization"> multi-period optimization</a> </p> <a href="https://publications.waset.org/abstracts/189031/leveraging-deep-q-networks-in-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189031.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">32</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">8515</span> User Requirements Analysis for the Development of Assistive Navigation Mobile Apps for Blind and Visually Impaired People</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paraskevi%20Theodorou">Paraskevi Theodorou</a>, <a href="https://publications.waset.org/abstracts/search?q=Apostolos%20Meliones"> Apostolos Meliones</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of the development process of two assistive navigation mobile apps for blind and visually impaired people (BVI) an extensive qualitative analysis of the requirements of potential users has been conducted. The analysis was based on interviews with BVIs and aimed to elicit not only their needs with respect to autonomous navigation but also their preferences on specific features of the apps under development. The elicited requirements were structured into four main categories, namely, requirements concerning the capabilities, functionality and usability of the apps, as well as compatibility requirements with respect to other apps and services. The main categories were then further divided into nine sub-categories. This classification, along with its content, aims to become a useful tool for the researcher or the developer who is involved in the development of digital services for BVI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accessibility" title="accessibility">accessibility</a>, <a href="https://publications.waset.org/abstracts/search?q=assistive%20mobile%20apps" title=" assistive mobile apps"> assistive mobile apps</a>, <a href="https://publications.waset.org/abstracts/search?q=blind%20and%20visually%20impaired%20people" title=" blind and visually impaired people"> blind and visually impaired people</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20requirements%20analysis" title=" user requirements analysis"> user requirements analysis</a> </p> <a href="https://publications.waset.org/abstracts/114395/user-requirements-analysis-for-the-development-of-assistive-navigation-mobile-apps-for-blind-and-visually-impaired-people" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114395.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">123</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=deep%20learning%20apps&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=deep%20learning%20apps&page=3">3</a></li> <li class="page-item"><a class="page-link" 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