LOTS: Litter On The Sand dataset for litter segmentation

The marine ecosystem is threatened by human waste released into the sea. One of the most challenging marine litter to identify and remove are the small particles settled on the sand which may be ingested by local fauna or cause damage to the marine ecosystem. Those particles are not easy to identify because they get confused with maritime/natural material, natural elements such as shells, stones or others, which can not be classified as "litter". In this work we present a dataset of Litter On The Sand (LOTS), with images of clean, dirty and wavy sand from 3 different beaches

SENECA: A Pedagogical Tool Supporting Remote Teaching and Learning

In this paper, we suggest SENECA, a tool that attempts to assist students who follow remote classes in maintaining/capturing attention, allowing them to focus on context-driven learning. Distance education has a number of disadvantages, including a lack of physical interaction between students and teachers, emotional and motivational isolation as a result of this strategy, and a reduction in active engagement. All of these things have an impact on student learning abilities. The largest distractions at home are considered among these disadvantages of distant education, particularly for subjects with low awareness. These distractions cause a movement of the student’s attention from the current lesson to disturbing events. For this reason, there is a need to experiment with new solutions also linked to Information Technology (IT) to improve the focused learning during distance education. Our tool’s technical idea is to create a real-time summary of the topic treated by the teacher. The system captures the text every five minutes, generates outlines, and browses them to eliminate repetitive portions after each survey. We looked at two different sorts of filters, semantic and summary, to see if the first could distinguish between topics and the second could evaluate the topic’s highlights. Natural Language Processing algorithms are used to extract categories and keywords from the general generated summary. The latter will emphasize the most important points of the speech, while the keywords will be utilized to extract the candidate literature about the discussed topics

PADD: Dynamic Distance-Graph based on Similarity Measures for GO Terms Visualization of Alzheimer and Parkinson diseases

In the biological field, having a visual and interactive representation of data is useful, particularly when there is a need to investigate a large amount of multilevel data. It is advantageous to communicate this knowledge intuitively because it helps the users to perceive the dynamic structure in which the correct connections are present and can be extrapolated. In this work, we propose a human-interaction system to view similarity data based on the functions of the Gene Ontology (Cellular Component, Molecular Function, and Biological Process) of the proteins/genes for Alzheimer disease and Parkinson disease. The similarity data was built with the Lin and Wang measures for all three areas of Gene Ontology. We clustered data with the K-means algorithm in order to demonstrate how information derived from data can only be partial when using traditional display methods. Then, we have suggested a dynamic and interactive view based on SigmaJS with the aim of allowing customization in the interactive mode of the analysis workflow by users. To this aim, we have developed a first prototype to obtained a more immediate visualization to capture the most relevant information within the three vocabularies of Gene Ontology. This facilitates the creation of an omic view and the ability to perform a multilevel analysis with more details which is much more valuable for the understanding of knowledge by the end users

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem

BackgroundMelanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients.ResultsTo achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16.ConclusionsThe results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured: 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet

A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates

Cold spray is a novel production technology for creating metallic layers on various materials. Using a pressurized gas travelling at supersonic speeds, the metallic particles are accelerated and impact the target surface obtaining adhesion through mechanical interlocking between the powders and the substrate. This method is especially well suited for coating thermosensitive materials like composites since it only requires a little amount of heat, as the powders remain in a solid state. The quality and comprehension of this manufacturing process can be greatly improved by using machine learning techniques. In order to evaluate the characteristics of the particle’s deformation upon collision, the goal of this work is to forecast it using machine learning approaches. The parameters chosen as an input for the model were related to 3 macro-categories: process parameters, powder parameters and substrate parameters. As regards the output parameters, flattening and penetration were chosen as they are the main characteristics of the coating on which homogeneity and adhesion depend. In order to obtain reliable results, a mix of data FEM and experimental data were used to train the neural network. The model was then tested on a dataset of experimental data