Alireza Sadeghian's research while affiliated with University of Toronto and other places
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Publications (136)
In this paper, a multi-fractal analysis on a diastolic blood pressure signal is conducted. The signal is measured in a time span of circa one day through the multifractal detrended fluctuation analysis framework. The analysis is performed on asymptotic timescales where complex regulating mechanisms play a fundamental role in the blood pressure stab...
Electric Arc Furnaces (EAFs) account for almost half of the North American steel production. Arc furnaces draw high and dynamic electrical power to melt scrap metal loads and as the result, they are highly non-linear and time-varying in nature. Because of the power system problems associated with EAFs, there is an ongoing need to accurately model E...
Bladder cancer tissue grading, which assigns a numerical grade reflecting how aggressive a tumor looks under a microscope, is essential to determine the proper course of treatment, design a therapeutic plan and determine prognosis. The major problem is that there are considerable and clinically relevant variations in grading by pathologists - as th...
Recent advances in semi-supervised learning algorithms (SSL) have made great strides in reducing the training dependency on labeled datasets and requiring that only a subset of the data be labeled. The presented work explores a class of semi-supervised learning algorithms that uses consistency regularization and self-ensembling to leverage the unla...
Today’s advancements have made financial markets accessible to everyone; hence, portfolio selection has become an individualized decision-making problem without the need of being highly educated. Individual judgments, however, are subjective and are influenced by the individual’s background, experience, and views. Existing methods do not account fo...
The objective of the work presented in this article is to investigate the applicability of lightweight machine learning (ML) algorithms capable of detecting and forecasting hypertensive (HT) episodes from historical intracranial pressure (ICP) signals. Specifically, we aim at identifying noncomputationally dependent algorithms, which can be support...
Annually, over three million people in North America suffer concussions. Every age group is susceptible to concussion, but youth involved in sporting activities are particularly vulnerable, with about 6% of all youth suffering a concussion annually. Youth who suffer concussion have also been shown to have higher rates of suicidal ideation, substanc...
Social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. To understand how information is diffused in these social networks, it is important to examine users’ online activities and behaviors. In this work, we focus on Twitter and study the impact of users’ behaviors on their retweet activities (the major...
Many concussions, the mildest form of TBI, go unreported; so the true incidence of TBI makes it the commonest or second most common neurological condition, next to migraines. A concussion can interfere with the transfer of information across the connecting axons in the brain that can be disrupted by TBI, thus resulting in a wide range of symptoms a...
We performed a PubMed search to find 148 papers published between January 2010 and December 2019 related to human brain, Diffusion Tensor Imaging (DTI), and Machine Learning (ML). The studies focused on healthy cohorts (n = 15), mental health disorders (n = 25), tumor (n = 19), trauma (n = 5), dementia (n = 24), developmental disorders (n = 5), mov...
Purpose:
To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients.
Method:
We presented an overview of the fundamental concepts in...
Defuzzification plays an important role in the applications of Interval Type-2 Fuzzy Sets (IT2FSs). However computational complexity of existing defuzzification methods has turned this procedure to be an important bottleneck toward the use of IT2FSs. It has been proved that the Nie-Tan method is an accurate discretizing based method for calculating...
Online social network is a great medium to express one’s opinion, sentiment, preference, and reaction on a topic. Tweets posted by Twitter users are used as a mechanism to share information. By retweeting a tweet, users not only approve the information provided by the tweet but also share the similar emotions and sentiment expressed by the tweet. A...
OBJECTIVE
Artificial neural networks (ANNs) have shown considerable promise as decision support tools in medicine, including neurosurgery. However, their use in concussion and postconcussion syndrome (PCS) has been limited. The authors explore the value of using an ANN to identify patients with concussion/PCS based on their antisaccade performance....
Retweeting or reposting a message is considered as an easily available information diffusion mechanism provided by Twitter or any other social network sites. By finding out why a user retweets a tweet, or predicting whether a tweet will be retweeted by a user, we can not only understand user's behavior or interest better, but also understand how in...
The current paper presents a data-driven detrending technique allowing to smooth complex sinusoidal trends from a real-world electric load time series before applying the Detrended Multifractal Fluctuation Analysis (MFDFA). The algorithm we call Smoothed Sort and Cut Fourier Detrending (SSC-FD) is based on a suitable smoothing of high power periodi...
Modeling and recognizing faults and outages in a real-world power grid is a challenging task, in line with the modern concept of Smart Grids. The availability of Smart Sensors and data networks allows to “x-ray scan” the power grid states. The present paper deals with a recognition system of fault states described by heterogeneous information in th...
Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthes...
In this work, we present a method for generating an adjacency matrix encoding a typical protein contact network. This work constitutes a follow-up to our recent work (Livi et al., 2015), whose aim was to estimate the relative contribution of different topological features in discovering of the unique properties of protein structures. We perform a g...
We propose a multi-agent algorithm able to automatically discover relevant
regularities in a given dataset, determining at the same time the set of
configurations of the adopted parametric dissimilarity measure yielding compact
and separated clusters. Each agent operates independently by performing a
Markovian random walk on a suitable weighted gra...
A large volume of data is steadily produced by the healthcare industry on daily basis. Data mining and machine learning approaches are two effective techniques applicable for data analysis and finding the hidden patterns which can be utilized for medical decision making. As the decisions in medical field are dealing with patient outcome, a high lev...
In a modern power grid known also as a Smart Grid (SG) its of paramount importance detecting a fault status both from the electricity operator and consumer feedback. The modern SG systems are equipped with Smart Sensors scattered within the real-world power distribution lines that are able to take a fine-grain picture of the actual power grid statu...
In this paper we propose a frameworks for identifying patterns and regularities in the pseudo-anonymized Call Data Records (CDR) pertaining a generic subscriber of a mobile operator. We face the challenging task of automatically deriving meaningful information from the available data, by using an unsupervised procedure of cluster analysis and witho...
Characterizations in terms of fractals are typically employed for systems with complex and multi-scale descriptions. A prominent example of such systems is provided by the human brain, which can be idealized as a complex dynamical system made of many interacting subunits. The human brain can be modeled in terms of observable variables together with...
We propose a system able to synthesize automatically a classification model and a set of interpretable decision rules defined over a set of symbols, corresponding to frequent substructures of the input dataset. Given a preprocessing procedure which maps every input element into a fully labeled graph, the system solves the classification problem in...
Computational Intelligence techniques are today widely used to solve complex engineering problems. Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems are nowadays adopted as hybrids techniques in the commercial and industrial environment. In this paper, we present an interesting application of the FUZZY-GA paradigm to Smart...
Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the p...
This chapter introduces a fuzzy disjointing differenceoperator. Based on the ordering of the disjoint fuzzy sets of the real line, a novel algorithm for calculation of the union and intersection of type-2 fuzzy sets with convex fuzzy grades using min t-norm and max t-conorm is proposed. The algorithm can be easily extended to the problems of orderi...
In this position paper we describe a general framework for applying machine
learning and pattern recognition techniques in healthcare. In particular, we
are interested in providing an automated tool for monitoring and incrementing
the level of awareness in the operating room and for identifying human errors
which occur during the laparoscopy surgic...
This paper presents an Extreme Learning Machine (ELM) time series prediction strategy to estimate the current and voltage behaviour of an Electric Arc Furnace (EAF). The proposed ELM predictor is designed for both long and short term predictions of the v-i characteristics of an EAF. The proposed predictor is evaluated using two real sensors' output...
The analysis and recognition of fault status in the Smart Grid field is a challenging problem. Computational Intelligence techniques have already been shown to be a successful framework to face complex problems related to a Smart Grid. The availability of huge amounts of data coming from smart sensors allows the system to take a fine grained pictur...
We approach the problem of forecasting the load of incoming calls in a cell of a mobile network using Echo State Networks. With respect to previous approaches to the problem, we consider the inclusion of additional telephone records regarding the activity registered in the cell as exogenous variables, by investigating their usefulness in the foreca...
The growth in context-aware systems and smart devices elevates another technology in ubiquitous computing — Internet of Things (IoT), where all objects are connected. The integration of Smart objects and social networking play an important role in today's life. This paper mainly promotes the management and architecture for adaptive social services...
This paper presents a universal methodology for generating an interval type-2 fuzzy set membership function from a collection of type-1 fuzzy sets. The key idea of the proposed methodology is to designate a specific type-1 fuzzy set as the representative of all input type-1 fuzzy sets. To this end, we use a novel measure of similarity between type-...
In this paper, we analyze 48 signals of rest tremor velocity related to 12
distinct subjects affected by Parkinson's disease. The subjects belong to two
different groups, formed by four and eight subjects with, respectively, high-
and low-amplitude rest tremors. Each subject is tested in four settings, given
by combining the use of deep brain stimu...
In this paper, we study long-term correlations and multifractal properties
elaborated from time series of three-phase current signals coming from an
industrial electric arc furnace plant. Implicit sinusoidal trends are suitably
detected in the scaling of the fluctuation function of such time series. Time
series are then initially filtered via a Fou...
In this paper we present a generative model for protein contact networks. The
soundness of the proposed model is investigated by focusing primarily on
mesoscopic properties elaborated from the spectra of the graph Laplacian. To
complement the analysis, we study also classical topological descriptors, such
as statistics of the shortest paths and the...
In the past few years, the advances in context-aware systems and sensor technologies, has elevated the Internet of Things (IoT) development greatly and rather quickly. Services of IoT systems must be reasonably designed to provide not only the user's requirements and requests, but also perceive the environmental context and customized services to g...
Data analysis techniques have been traditionally conceived to cope with data described in terms of numeric vectors. The reason behind this fact is that numeric vectors have a well-defined and clear geometric interpretation, which facilitates the analysis from the mathematical viewpoint. However, the state-of-the-art research on current topics of fu...
Three-dimensional quantitative ultrasound spectroscopic imaging of prostate was investigated clinically for the noninvasive detection and extent characterization of disease in cancer patients and compared to whole-mount, whole-gland histopathology of radical prostatectomy specimens. Fifteen patients with prostate cancer underwent a volumetric trans...
Power losses reduction is one of the main targets for any electrical energy
distribution company. In this paper, we face the problem of joint optimization
of both topology and network parameters in a real smart grid. We consider a
portion of the Italian electric distribution network managed by the ACEA
Distribuzione S.p.A. located in Rome. We perfo...
In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approa...
Frontiers of Higher Order Fuzzy Sets, provides a unified representation theorem for higher order fuzzy sets. The book elaborates on the concept of gradual elements and their integration with the higher order fuzzy sets. This book also is devoted to the introduction of new frameworks based on general T2FSs, IT2FSs, Gradual elements, Shadowed sets an...
The Computational Intelligence paradigm has proven to be a useful approach when facing problems related to Smart Grids (SG). The modern SG systems are equipped with Smart Sensors scattered in the real-world power distribution lines that are able to take a fine-grained picture of the actual power grid state gathering a huge amount of heterogeneous d...
The multifractal detrended fluctuation analysis of time series is able to
reveal the presence of long-range correlations and, at the same time, to
characterize the self-similarity of the series. The rich information derivable
from the characteristic exponents and the multifractal spectrum can be further
analyzed to discover important insights about...
In this paper we study the structure of three types of biochemical networks:
protein, metabolic, and gene expression networks, together with simulated
archetypical networks acting as probes. We consider both classical topological
descriptors, such as the modularity and statistics of the shortest paths, and
different interpretations in terms of diff...
![Table 1: Considered IAM datasets. See [63] for details.](profile/Antonello-Rizzi/publication/265015352/figure/tbl1/AS:392132985671683@1470503197838/Considered-IAM-datasets-See-63-for-details_Q320.jpg)
Representing patterns by complex relational structures, such as labeled
graphs, is becoming an increasingly common practice in the broad field of
computational intelligence. Accordingly, a wide repertoire of pattern
recognition tools, such as classifiers and knowledge discovery procedures, are
nowadays available and tested for various labeled graph...
We evaluate a version of the recently-proposed Optimized Dissimilarity Space
Embedding (ODSE) classification system that operates in the input space of
sequences of generic objects. The ODSE system has been originally presented as
a labeled graph classification system. However, since it is founded on the
dissimilarity space representation of the in...
![Table 1 -Data used in recommendation systems [2]](profile/Asad-Madni/publication/266970881/figure/tbl1/AS:613507376635940@1523282965754/Data-used-in-recommendation-systems-2_Q320.jpg)
![Figure 1-Part of Recommendation Systems [4]](profile/Asad-Madni/publication/266970881/figure/fig1/AS:669163794210822@1536552489496/Part-of-Recommendation-Systems-4_Q320.jpg)
Internet is speeding up and modifying the manner in which daily tasks such as online shopping, paying utility bills, watching new movies, communicating, etc., are accomplished. As an example, in older shopping methods, products were mass produced for a single market and audience but that approach is no longer viable. Markets based on long product a...
This paper deals with the relations among structural, topological, and
chemical properties of the E.Coli proteome from the vantage point of the
solubility/aggregation propensities of proteins. Each E.Coli protein is
initially represented according to its known folded 3D shape. This step
consists basically in representing the available E.Coli protei...
![Table 4: The considered IAM datasets. Full details in [53].](profile/Lorenzo-Livi/publication/264349276/figure/tbl2/AS:392425878114304@1470573028010/The-considered-IAM-datasets-Full-details-in-53_Q320.jpg)
The one-class classification problem is a well-known research endeavor in
pattern recognition. The problem is also known under different names, such as
outlier and novelty/anomaly detection. The core of the problem consists in
modeling and recognizing patterns belonging only to a so-called target class.
All other patterns are termed non-target, and...
Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical condition of the elements forming
the whole infrastructure (e.g., cables and...
Due to the intrinsic complexity of real-world power distribution lines, which are highly non-linear and time-varying systems, modeling and predicting a general fault instance is a very challenging task. Power outages can be experienced as a consequence of a multitude of causes, such as damage of some physical components or grid overloads. Smart gri...
In this paper, we deal with the problem of classification of interval type-2 fuzzy sets through evaluating their distinguishability. To this end, we exploit a general matching algorithm to compute their similarity measure. The algorithm is based on the aggregation of two core similarity measures applied independently on the upper and lower membersh...
Research on Graph-based pattern recognition and Soft Computing systems has attracted many scientists and engineers in several different contexts. This fact is motivated by the reason that graphs are general structures able to encode both topological and semantic information in data. While the data modeling properties of graphs are of indisputable p...
In this paper, we propose a novel two-phase methodology based on interval type-2 fuzzy sets (T2FSs) to model the human perceptions of the linguistic terms used to describe the online services satisfaction. In the first phase, a type-1 fuzzy set (T1FS) model of an individual's perception of the terms used in rating user satisfaction is derived throu...
The existing methods of determining an α-cut of a fuzzy set to construct its underlying shadowed set do not fully comply with the concept of shadowed sets, namely, a retention of the total amount of fuzziness and its localized redistribution throughout a universe of discourse. Moreover, no closed formula to calculate the corresponding α-cut is avai...
Purpose:
Currently, no clinical imaging modality is used routinely to assess tumor response to cancer therapies within hours to days of the delivery of treatment. Here, the authors demonstrate the efficacy of ultrasound at a clinically relevant frequency to quantitatively detect changes in tumors in response to cancer therapies using preclinical m...
We propose two variants of a general-purpose graph classification system which rely on a theoretical result that we prove in this paper. The result allows us to solve analytically the setting of a sequential clustering algorithm that is used for compressing the input labeled graphs represented in the dissimilarity space. As a consequence, we achiev...
In this paper, two novel image search mechanisms are introduced and compared against the traditional linear approach and one another. These feature detection algorithms mimic two natural human search strategies which make them suitable for faster feature detection within pixelated images. Unlike linear approaches where pixel-by-pixel probing is req...
Specific characteristics of human perception, like context-dependency, imprecision, and diversity, demand capable formal frameworks for modeling the human mind. This chapter discusses a two-phase method for deriving type-2 fuzzy sets that model human perceptions of the linguistic terms used in describing online satisfaction. In the first phase, we...
This paper proposes a novel way of matching general type-2 fuzzy sets using a sequence-based approach. General sequences are defined as an ordered list of objects, which are called events. In our contribution, an event of the sequenced type-2 fuzzy set is defined as the footprint of uncertainty of a specific α-plane. Suited matching algorithms for...
In this paper, we propose a procedure for computing the dissimilarity measure of finite general type-2 fuzzy sets, represented as sequences of vertical slices. Through representing general type-2 fuzzy sets as a sequence of objects, we compute their overall dissimilarity value using suited matching algorithms for generalized sequences. The evaluati...
In this chapter, the concept of Shadowed Fuzzy Set is introduced and some of its related operations are studied. Shadowed Fuzzy Set enables localization of the underlying uncertainty of fuzzy grades in type-2 fuzzy sets through exploitation of shadowed sets. It provides a capable framework that despite preserving the uncertainties of fuzzy grades i...
Online tourism is one of the most successful e-commerce implementations (Turban et al., 2010) and therefore investigating its success factors has increasing importance. This paper investigates the determinants of tourist satisfaction in on-line tourism. To this end, a factor analysis was performed. The results confirmed the five factor model illust...
The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for narrow application do-mains, they frequently overlap with other ontologies in broader domains...
This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-...
This paper is intended to present a method to find an optimized route with intelligent devices for vehicles. Because the vehicles routing problem is one of the possible applications in which the demands of the driver are not specified, this proposed method will use learning automata and fuzzy logics in dynamic environment in order to learn user beh...
This paper introduces median interval approach MIA as a simple systematic method for modelling words from natural languages with interval type-2 fuzzy sets IT2FS. The methodology is based on calculating the median boundaries of the range of membership functions associated with the words. MIA exhibits outlier tolerance which makes it applicable on d...