Automatic Identification and Classification of Misogynistic Language on Twitter

2018 ◽  
pp. 57-64 ◽  
Author(s):  
Maria Anzovino ◽  
Elisabetta Fersini ◽  
Paolo Rosso
Keyword(s):  
Automatic Identification

Deep Learning-Based Classification of Malignant and Benign Cells in Dermatoscopic Images via Transfer Learning Approach

2021 ◽  
Author(s):  
V. Akash Kumar ◽  
Vijaya Mishra ◽  
Monika Arora
Keyword(s):  
Skin Cancer ◽  
Transfer Learning ◽  
Human Life ◽  
Skin Pigmentation ◽  
Automatic Identification ◽  
Learning Approach ◽  
Predicted Model ◽  
Classification Tasks ◽  
Cancerous Cells

The inhibition of healthy cells creating improper controlling process of the human body system indicates the occurrence of growth of cancerous cells. The cluster of such cells leads to the development of tumor. The observation of this type of abnormal skin pigmentation is done using an effective tool called Dermoscopy. However, these dermatoscopic images possess a great challenge for diagnosis. Considering the characteristics of dermatoscopic images, transfer learning is an appropriate approach of automatically classifying the images based on the respective categories. An automatic identification of skin cancer not only saves human life but also helps in detecting its growth at an earlier stage which saves medical practitioner’s effort and time. A newly predicted model has been proposed for classifying the skin cancer as benign or malignant by DCNN with transfer learning and its pre-trained models such as VGG 16, VGG 19, ResNet 50, ResNet 101, and Inception V3. The proposed methodology aims at examining the efficiency of pre-trained models and transfer learning approach for the classification tasks and opens new dimensions of research in the field of medicines using imaging technique which can be implementable in real-time applications.

Automatic identification and classification of the northern part of the Red Sea trough and its application for climatological analysis

2019 ◽  
Vol 40 (7) ◽  
pp. 3607-3622 ◽  
Author(s):  
Hadas Saaroni ◽  
Tzvi Harpaz ◽  
Pinhas Alpert ◽  
Baruch Ziv
Keyword(s):  
Red Sea ◽  
Automatic Identification

Classification of vessel motion pattern in inland waterways based on Automatic Identification System

2018 ◽  
Vol 161 ◽  
pp. 69-76 ◽  
Author(s):  
Zhijun Chen ◽  
Jie Xue ◽  
Chaozhong Wu ◽  
LingQiao Qin ◽  
Liqun Liu ◽  
...  
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Motion Pattern ◽  
Inland Waterways ◽  
Automatic Identification System ◽  
Vessel Motion

Application of computer vision in the automatic identification and classification of woven fabric weave patterns

2010 ◽  
Vol 80 (20) ◽  
pp. 2144-2157 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Chung-Yang Shih ◽  
Cheng-En Ho ◽  
Kai-Ching Peng
Keyword(s):  
Computer Vision ◽  
Woven Fabric ◽  
Automatic Identification

Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients

2014 ◽  
Vol 22 (3) ◽  
pp. 685-694 ◽  
Author(s):  
Milica D. Djuric-Jovicic ◽  
Nenad S. Jovicic ◽  
Sasa M. Radovanovic ◽  
Iva D. Stankovic ◽  
Mirjana B. Popovic ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
Automatic Identification

scholarly journals Automatic Identification of Algal Community from Microscopic Images

2013 ◽  
Vol 7 ◽  
pp. BBI.S12844 ◽  
Author(s):  
Natchimuthu Santhi ◽  
Chinnaraj Pradeepa ◽  
Parthasarathy Subashini ◽  
Senthil Kalaiselvi
Keyword(s):  
Image Processing ◽  
Machine Learning Algorithms ◽  
Algal Community ◽  
Automatic Identification ◽  
Object Boundary ◽  
Algal Communities ◽  
Processing Application ◽  
Segmentation Methods ◽  
Processing Techniques

A good understanding of the population dynamics of algal communities is crucial in several ecological and pollution studies of freshwater and oceanic systems. This paper reviews the subsequent introduction to the automatic identification of the algal communities using image processing techniques from microscope images. The diverse techniques of image preprocessing, segmentation, feature extraction and recognition are considered one by one and their parameters are summarized. Automatic identification and classification of algal community are very difficult due to various factors such as change in size and shape with climatic changes, various growth periods, and the presence of other microbes. Therefore, the significance, uniqueness, and various approaches are discussed and the analyses in image processing methods are evaluated. Algal identification and associated problems in water organisms have been projected as challenges in image processing application. Various image processing approaches based on textures, shapes, and an object boundary, as well as some segmentation methods like, edge detection and color segmentations, are highlighted. Finally, artificial neural networks and some machine learning algorithms were used to classify and identifying the algae. Further, some of the benefits and drawbacks of schemes are examined.

scholarly journals The method of automatic identification of motor vehicle users

2020 ◽  
Vol 2 (2) ◽  
pp. 63-71
Author(s):  
Tadeusz Niedziela
Keyword(s):  
Fourier Transform ◽  
Physical Properties ◽  
Motor Vehicle ◽  
Automatic Recognition ◽  
Automatic Identification ◽  
Image Function ◽  
Characteristic Features ◽  
The Fourier Transform

This paper presents a method of automatic recognition of fingerprint diffraction images of motor vehicle users. The proposed method is based on the basic physical properties of the Fourier transform. It creates the possibility of reducing the problem of recognition to the Fourier transform of the image function, extraction of characteristic features vector and classification of input images.

scholarly journals Classification of Historical Documents Based on LBP and LPQ Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 1534-1539
Keyword(s):  
Support Vector ◽  
Historical Documents ◽  
Automatic Identification ◽  
Document Images ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Modern Language ◽  
Handwritten Document ◽  
Feature Values

Historical documents are important source for knowing culture, language, social activities, educational system, etc. The historical documents are in different languages and evolved over centuries and transformed to present modern language, classification of documents into various eras, recognition of words etc. In this paper, we have proposed a new approach to automatic identification of the age of the historical handwritten document images based on LBP (Local Binary Pattern) and LPQ (Local Phase Quantization) algorithm. The standard historical handwritten document images named as MPS (Medieval Paleographic Scale) dataset which is publicly available is used to experiment. LBP and LPQ descriptors are used to extract the features of the historical document images. Further, documents are classified based on the discriminating feature values using classifiers namely K-NN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifier. The accuracy of historical handwritten document images by K-NN and SVM are 90.7% and 92.8% respectively.

scholarly journals A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

2018 ◽  
Vol 25 (s1) ◽  
pp. 14-21 ◽  
Author(s):  
Rafał Szłapczyński ◽  
Tacjana Niksa-Rynkiewicz
Keyword(s):  
Risk Measures ◽  
Risk Measure ◽  
Near Miss ◽  
Fuzzy Classification ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Neuro Fuzzy ◽  
Fuzzy Network

Abstract Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near-miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.

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