Self-supervised learning methods and applications in medical imaging analysis: A survey

The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative, and contrastive approaches. Moreover, the article covers 40 of the most recent research papers in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Finally, the article concludes with possible future research directions in the field

Automatic recognition of Arabic alphabets sign language using deep learning

Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know

Views for Interoperability in a Heterogeneous Object-Oriented Multidatabase System

The research reported in this thesis is concerned with supporting interoperability among sets of pre-existing and heterogeneous object-oriented databases without forcing them to conform to a common data model, query language and DBMS, i.e. these databases preserve their local autonomy. Special attention has been paid to logically heterogeneous object-oriented databases - where heterogeneity arises through independent design. We have focussed on supporting multiple integration views over the participating databases - as different users have different reasons for integration and the same user may need to integrate the same set of local integration units in various ways to suit different roles or tasks. Thus this research avoids the rigidity of a one-toone correspondence between merging rules and local integration units, and allows local conflicts to be reconciled in various ways according to user requirements and preferences. To this end, we have designed a schema integration language ca..

A Framework for Predicting Proteins 3D Structures

This paper proposes a framework for predicting protein three dimensional structures from their primary sequences. The proposed method utilizes the natural multi-label and hierarchical intrinsic nature of proteins to build a multi-label and hierarchical classifier for predicting protein folds. The classifier predicts protein folds in two stages, at the first stage, it predicts the protein structural class, and in the second stage, it predicts the protein fold. When comparing our technique with SVM, naive Bayes, and boosted C4.5 we get a higher accuracy more than SVM and better than naive Bayes when using the composition, secondary structure and hydrophobicity feature attributes, and give higher accuracy than C4.5 when using composition, secondary structure, hydrophobicity, and polarity feature attributes. MuLAM was used as a basic classifier in the hierarchy of the implemented framework. Two major modifications were made to MuLAM, namely: the pheromone update and term selection strategies of MuLAM were altered

Automatic keyphrase extraction for Arabic news documents based on KEA system

A keyphrase is a sequence of words that play an important role in the identification of the topics that are embedded in a given document. Keyphrase extraction is a process which extracts such phrases. This has many important applications such as document indexing, document retrieval, search engines, and document summarization. This paper presents a framework for extracting keyphrases from Arabic news documents which is based on the KEA system. It relies on supervised learning, Naïve Bayes in particular, to extract keyphrases. Two probabilities are computed: the probability of being a keyphrase and the probability of not being a keyphrase. The final set of keyphrases is chosen from the set of phrases that have high probabilities of being keyphrases. The novel contributions of the current work are that it provides insights on keyphrase extraction for news documents written in Arabic. It also presents an annotated dataset that was used in the experimentation. Finally, it uses Naïve Bayes as a medium for extracting keyphrases.Scopu

A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

Sentiment analysis has drawn considerable interest among researchers owing to the realization of its fascinating commercial and business benefits. This paper deals with sentiment analysis in Arabic text from three perspectives. First, several alternatives of text representation were investigated. In particular, the effects of stemming, feature correlation and n-gram models for Arabic text on sentiment analysis were investigated. Second, the behaviour of three classifiers, namely, SVM, Naive Bayes, and K-nearest neighbour classifiers, with sentiment analysis was investigated. Third, the effects of the characteristics of the dataset on sentiment analysis were analysed. To this end, we applied the techniques proposed in this paper to two datasets; one was prepared in-house by the authors and the second one is freely available online. All the experimentation was done using Rapidminer. The results show that our selection of preprocessing strategies on the reviews increases the performance of the classifiers.Scopu

Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization

This paper compares and contrasts two feature selection techniques when applied to Arabic corpus; in particular; stemming, and light stemming were employed. With stemming, words are reduced to their stems. With light stemming, words are reduced to their light stems. Stemming is aggressive in the sense that it reduces words to their 3-letters roots. This affects the semantics as several words with different meanings might have the same root. Light stemming, by comparison, removes frequently used prefixes and suffixes in Arabic words. Light stemming doesn't produce the root and therefore doesn't affect the semantics of words; it maps several words, which have the same meaning to a common syntactical form. The effectiveness of above two feature selection techniques was assessed in a text categorization exercise for Arabic corpus. This corpus consists of 15000 documents that fall into three categories. The K-nearest neighbors (KNN) classifier was used in this work. Several experiments were carried out using two different representations of the same corpus; the first version uses stem- vectors; and the second uses light stem-vectors as representatives of documents. These two representations were assessed in terms of size, time and accuracy. The light stem representation was superior in terms of classifier accuracy when compared with stemming

Data Mining, Reasoning and Incremental Information Retrieval through Non Enlargeable Rectangular Relation Coverage

Association rules extraction from a binary relation as well as reasoning and information retrieval are generally based on the initial representation of the binary relation as an adjacency matrix. This presents some inconvenience in terms of space memory and knowledge organization. A coverage of a binary relation by a minimal number of non enlargeable rectangles generally reduces memory space consumption without any loss of information. It also has the advantage of organizing objects and attributes contained in the binary relation into a conceptual representation. In this paper, we propose new algorithms to extract association rules (i.e. data mining), conclusions from initial attributes (i.e. reasoning), as well as retrieving the total objects satisfying some initial attributes, by using only the minimal coverage. Finally we propose an incremental approximate algorithm to update a binary relation organized as a set of non enlargeable rectangles. Two main operations are mostly used during the organization process: First, separation of existing rectangles when we delete some pairs. Second, join of rectangles when common properties are discovered, after addition or removal of elements from a binary context. The objective is the minimization of the number of rectangles and the maximization of their structure. The article also raises the problems of equational modeling of the minimization criteria, as well as incrementally providing equations to maintain them