Peripheral Blood Smear Analyses Using Deep Learning

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by hematologists to assess some aspects of humans’ health status. PBS analysis is prone to human errors and utilizing computer-based analysis can greatly enhance this process in terms of accuracy and cost. Recent approaches in learning algorithms, such as deep learning, are data hungry, but due to the scarcity of labeled medical images, researchers had to find viable alternative solutions to increase the size of available datasets. Synthetic datasets provide a promising solution to data scarcity, however, the complexity of blood smears’ natural structure adds an extra layer of challenge to its synthesizing process. In this thesis, we propose a method- ology that utilizes Locality Sensitive Hashing (LSH) to create a novel balanced dataset of synthetic blood smears. This dataset, which was automatically annotated during the gener- ation phase, covers 17 essential categories of blood cells. The dataset also got the approval of 5 experienced hematologists to meet the general standards of making thin blood smears. Moreover, a platelet classifier and a WBC classifier were trained on the synthetic dataset. For classifying platelets, a hybrid approach of deep learning and image processing tech- niques is proposed. This approach improved the platelet classification accuracy and macro- average precision from 82.6% to 98.6% and 76.6% to 97.6% respectively. Moreover, for white blood cell classification, a novel scheme for training deep networks is proposed, namely, Enhanced Incremental Training, that automatically recognises and handles classes that confuse and negatively affect neural network predictions. To handle the confusable classes, we also propose a procedure called "training revert". Application of the proposed method has improved the classification accuracy and macro-average precision from 61.5% to 95% and 76.6% to 94.27% respectively. In addition, the feasibility of using animal reticulocyte cells as a viable solution to com- pensate for the deficiency of human data is investigated. The integration of animal cells is implemented by employing multiple deep classifiers that utilize transfer learning in differ- ent experimental setups in a procedure that mimics the protocol followed in experimental medical labs. Moreover, three measures are defined, namely, the pretraining boost, the dataset similarity boost, and the dataset size boost measures to compare the effectiveness of the utilized experimental setups. All the experiments of this work were conducted on a novel public human reticulocyte dataset and the best performing model achieved 98.9%, 98.9%, 98.6% average accuracy, average macro precision, and average macro F-score re- spectively. Finally, this work provides a comprehensive framework for analysing two main blood smears that are still being conducted manually in labs. To automate the analysis process, a novel method for constructing synthetic whole-slide blood smear datasets is proposed. Moreover, to conduct the blood cell classification, which includes eighteen blood cell types and abnormalities, two novel techniques are proposed, namely: enhanced incremental train- ing and animal to human cells transfer learning. The outcomes of this work were published in six reputable international conferences and journals such as the computers in biology and medicine and IEEE access journals

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

Vox-Exo: Horrors of a Voice

Vox-Exo: Horrors of a Voice reframes the Lacanian object a voice as a horrific register of alterity. The object gaze has received, as it does in Jacques Lacan’s work, more commentary than voice. Yet recently voice has garnered interest from multiple disciplines. The thesis intervenes in the Slovenian school’s commentary of the ‘object voice’ in terms of two questions: audition and corporeality. This intervention synthesizes psychoanalysis with recent theorizing of the horror of philosophy. In this intervention the object a voice is argued to resonate in lacunae – epistemological voids that evoke horror in the subject. Biological and evolutionary perspectives on voice, genre horror film and literature, music videos, close readings of Freudian and Lacanian case studies and textual analysis of ancient philosophy texts all contribute to an elucidation of the horrors of the object a voice: Vox-Exo. The impetus to address the body stems from a critical intervention in one of the most recent and cited works on voice in Lacan’s work; A Voice and Nothing More, by Mladen Dolar. Dolar’s trajectory to pursue the ‘object voice’ is to move from meaning, to aesthetics, to psychoanalysis. Such a move, the expedited turn to Lacanian psychoanalysis, is recalcitrant to tackling the body. This thesis responds to the book’s avidity to omit the body from the question of voice. Vox-Exo: Horrors of a Voice, contra the Slovenian School’s credo, rearticulates the object a voice in terms of corporeality and audition. The significance the a, that designates other, autre, stated in Lacan’s works, and numerous commentaries such as Jacques-Alain Miller’s and Stijn Vanheule’s, is impressed. With a sustained consideration of corporeality and audition, the unknowability and alterity of a is demonstrated to be a locus of horror. Object a voice is argued to resonate in lacunae, evoking legion, indefinite, horror – a horror

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition