Graph-based methods for Significant Concept Selection

It is well known in information retrieval area that one important issue is the gap between the query and document vocabularies. Concept-based representation of both the document and the query is one of the most effective approaches that lowers the effect of text mismatch and allows the selection of relevant documents that deal with the shared semantics hidden behind both. However, identifying the best representative concepts from texts is still challenging. In this paper, we propose a graph-based method to select the most significant concepts to be integrated into a conceptual indexing system. More specifically, we build the graph whose nodes represented concepts and weighted edges represent semantic distances. The importance of concepts are computed using centrality algorithms that levrage between structural and contextual importance. We experimentally evaluated our method of concept selection using the standard ImageClef2009 medical data set. Results showed that our approach significantly improves the retrieval effectiveness in comparison to state-of-the-art retrieval models

Classification of MRI brain tumors based on registration preprocessing and deep belief networks

In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%

A Granular Computing Classifier for Human Activity with Smartphones

Recently, smart home devices have been widely used to assist and facilitate the lives of human beings. Human activity recognition (HAR) aims to identify human activities using sensors in smartphones. Therefore, it can be employed in many applications, such as remote health monitoring for disabled and elderly people. This paper proposes a granular computing-based approach to classifying human activities using wearable sensing devices. The approach has two main phases: feature selection and classification. In the feature selection phase, the approach attempts to remove redundant and irrelevant attributes. At the same time, the classification phase makes use of granular computing concepts to build the granules and find the relationships between granules at different levels. To evaluate the approach, we applied the dataset to five famous machine learning models. For the comparative evaluation, we also tested other well-known machine learning methods. The experimental results presented in this paper show that the approach outperformed common traditional classifiers in terms of classification precision recall, f-measure, and MCC for most recognized human activities by approximately 97.3%, 94%, 95.5%, and 94.8%, respectively. However, in terms of processing time, it performs comparably

Radiogenomics in prostate cancer evaluation

International audiencePURPOSE OF REVIEW: Radiogenomics, fusion between radiomics and genomics, represents a new field of research to improve cancer comprehension and evaluation. In this review, we give an overview of radiogenomics and its most recent and relevant applications in prostate cancer management. RECENT FINDINGS: Literature about radiogenomics in prostate cancer emerged last 5 years but remains scarce. Radiogenomics in prostate cancer mainly rely on MRI-based features. Several imaging biomarkers, mostly based on the identification of radiomic features from deep learning studies, have been studied for the prediction of genomic profiles, such as PTEN Decipher Oncotype DX or Prolaris expression. However, despite promising results, several limitations still preclude any integration of radiogenomics in daily practice. SUMMARY: In the future, the emergence of artificial intelligence in urology, with an increasing use of radiomics and genomics data, may enable radiogenomics to assume a growing role in the evaluation of prostate cancer, with a noninvasive and personal approach in the field of personalized medicine. Further efforts are necessary for integration of this promising approach in prostate cancer decision-making

Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix

Bulletin of EEI Stats

In industrials process and many studies cases, state and output derivative variables can be easily modeled in reciprocal state space (RSS) form than the standard one. Formulation of stabilization problem with a guaranteed cost control using feedback principle for Lipschitz nonlinear systems (LNS) in RSS is presented in this brief. The asymptotic stability, using the proper Lyapunov functions of the closed-loop system, is guaranteed. The control design problem is guaranteed through a resolution of linear matrix inequalities (LMI) technique under certain lemmas and minimization of non-standard cost control. Experimental validation shows the good performances of the proposed method using real-time implementation (RTI) with a digital signal processing (DSP) device (Arduino MEGA 2560)

Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix