BigDatawebPage/index.html at master · ABPLab/BigDatawebPage · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
<!doctype html>
<html lang="en">
  <head>
    <!-- Required meta tags -->
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

    <!-- Bootstrap CSS -->
    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css" integrity="sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh" crossorigin="anonymous">
    <link rel="stylesheet" href="css/main.css">

    <title>Big Data for good</title>
  </head>
  <body>
      <div class='bigimage'>
      <nav class="navbar navbar-dark">
          <span class="navbar-brand mb-0 h1">Big Data for Good</span>
      </nav>
        <div style="margin-top: 10%">
            <h1>We use Artificial Intelligence<br>for good</h1><br>
            <button  type="button" class="btn btn-primary btn-lg" onclick="document.getElementById('main').scrollIntoView({ behavior: 'smooth', block: 'center' });">Read More </button>
        </div>

      </div>

      <main id='main'>
        <div class="container">
            <p>At the <a href="http://abp.dipsco.unitn.it/" target="_blank">Affiliative Behaviour and Physiology Lab</a> and at the <a href="https://blogs.ntu.edu.sg/sanlab/" target="_blank">Social and Affective Neuroscience Lab</a> we employ novel Artificial Intelligence technqique and large scale Cloud Computing models for the analysis of neurophysiological signals and behavioural data.</p>

            <p>Our practices are based on an <strong>Open Science</strong> approach, where data are made available, togheter with our softwares and homemade analysis tools, to allow for reproducibility of our results and to demonstrate the quality of our work.</p>

            <p>Thanks to our international collaborations, we create new knowledge trough the application of novel <strong>Machine Learning and Neural Networks</strong> models to different types of big data.</p>
        </div>

        <div id='CMcontainer'>
            <div class='container'>
                <h2>Child Mortality in Low and Middle Income Countries</h2>
                <p>Child mortality still affects 3.9% of children below five years old worldwide with a large and worrisome disparity between high- (0.9%) and low-income countries (7.5% in African countries). The objective of the study is to identify risk factors associated with Child Mortality, trough the analysis of the <a href='http://mics.unicef.org/' target="_blank" rel="nofollow">MICS (Multiple Indicator Cluster Survey)</a>, a dataset created by UNICEF.</p>

                <p>We adopted the Micro-Array Quality Control Data Analysis Plan (MAQC-DAP) on Random Forest models to identify Child Mortality's Risk Factors.</p>

                <p>Our results reveal how the age of the mother, the availability of electricy within the household, and the highest level of education of the mother are amongst the top factors that have an influence on child mortality.</p>

                <div class="row">
                <div class="col-sm">
                    <h3>Full Report</h3>
                    <p>Our full report will be available soon.</p>
                </div>
                <div class="col-sm">
                    <h3>Talks and Posters</h3>
                    <ul>
                        <li>Bizzego, A., Gabrieli, G., Bornstein, M. H., Deater-Deckard, K., Putnick, D. L., Lansford, J. E., Bradley, R. H., Costa, M. and Esposito, G., (2019). Child Mortality in Low- and Middle-Income Countries: A transdisciplinary study using Artificial Intelligence, 3rd Interdisciplinary Developmental Pediatrics Association Confress, 9-12 December 2019, Manila, Philippines</li>
                        <li>Bizzego, A., Gabrieli, G., Bornstein, M. H., Deater-Deckard, K., Putnick, D. L., Lansford, J. E., Bradley, R. H., Costa, M. and Esposito, G., (2019). Adapting the MAQC-DAP to the UNICEF MICS Dataset to Predict Child Mortality in Low- and Middle-Income Countries, 3rd Annual MAQC Society Conference, 8-10 April 2019, Trento, Italy</li>
                    </ul>

                </div>
          </div>
            </div>
        </div>
    </main>

    <footer class="">
        <div class="container">
            <div class="row">
                <div class="col-sm">
                    <h4>Affiliative Behaviour and Physiology Lab</h4>
                    <i>Department of Psychology and Cognitive Science<br>University of Trento (Italy)</i><br>
                    <a href="http://abp.dipsco.unitn.it/" target="_blank">Affiliative Behaviour and Physiology Lab</a>
                </div>
                <div class="col-sm">
                    <h4>Social and Affective Neuroscience Lab</h4>
                    <i>Psychology Programme, School of Social Sciences<br>
                    Nanyang Technological University (Singapore)</i><br>
                    <a href="https://blogs.ntu.edu.sg/sanlab/" target="_blank">Social and Affective Neuroscience Lab</a>
                </div>
          </div>
        </div>
    </footer>

    <!-- Optional JavaScript -->
    <!-- jQuery first, then Popper.js, then Bootstrap JS -->
    <script src="https://code.jquery.com/jquery-3.4.1.slim.min.js" integrity="sha384-J6qa4849blE2+poT4WnyKhv5vZF5SrPo0iEjwBvKU7imGFAV0wwj1yYfoRSJoZ+n" crossorigin="anonymous"></script>
    <script src="https://cdn.jsdelivr.net/npm/popper.js@1.16.0/dist/umd/popper.min.js" integrity="sha384-Q6E9RHvbIyZFJoft+2mJbHaEWldlvI9IOYy5n3zV9zzTtmI3UksdQRVvoxMfooAo" crossorigin="anonymous"></script>
    <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/js/bootstrap.min.js" integrity="sha384-wfSDF2E50Y2D1uUdj0O3uMBJnjuUD4Ih7YwaYd1iqfktj0Uod8GCExl3Og8ifwB6" crossorigin="anonymous"></script>
  </body>
</html>