Browsing Scientific Publications in Information and Communication Technology by Author "Yazidi, Anis"
Now showing items 1-20 of 49
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A hierarchical learning scheme for solving the Stochastic Point Location problem
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John; Goodwin, Morten (Lecture Notes in Computer Science;7345, Chapter; Peer reviewed, 2012)This paper deals with the Stochastic-Point Location (SPL) problem. It presents a solution which is novel in both philosophy and strategy to all the reported related learning algorithms. The SPL problem concerns the task ... -
A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm
Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (Chapter; Peer reviewed, 2015) -
A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search
Yazidi, Anis; Oommen, John (Peer reviewed; Journal article, 2017)The most fundamental problem encountered in the field of stochastic optimization and control, is the Stochastic Root Finding (SRF) problem where the task is to locate (or in the context of control, to move towards), an ... -
A pattern recognition approach for peak prediction of electrical consumption
Goodwin, Morten; Yazidi, Anis (Journal article; Peer reviewed, 2016) -
A Stochastic Search on the Line-Based Solution to Discretized Estimation
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John (Lecture Notes in Computer Science;7345, Chapter; Peer reviewed, 2012)Recently, Oommen and Rueda [11] presented a strategy by which the parameters of a binomial/multinomial distribution can be estimated when the underlying distribution is nonstationary. The method has been referred to as the ... -
A user-centric approach for personalized service provisioning in pervasive environments
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John; Gerdes, Martin; Reichert, Frank (Journal article; Peer reviewed, 2011)The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention ... -
Achieving Fair Load Balancing by Invoking a Learning Automata-based Two Time Scale Separation Paradigm
Yazidi, Anis; Hassan, Ismail; Hammer, Hugo Lewi; Oommen, B. John (Journal article; Peer reviewed, 2020)In this article, we consider the problem of load balancing (LB), but, unlike the approaches that have been proposed earlier, we attempt to resolve the problem in a fair manner (or rather, it would probably be more appropriate ... -
Achieving Intelligent Traffic-aware Consolidation of Virtual Machines in a Data Center Using Learning Automata
Jobava, Akaki; Yazidi, Anis; Oommen, John; Begnum, Kyrre (Chapter; Peer reviewed, 2016) -
An adaptive approach to learning the preferences of users in a social network using weak estimators
Oommen, B. John; Yazidi, Anis; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2012)Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to ... -
An intelligent architecture for service provisioning in pervasive environments
Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John; Reichert, Frank; Gerdes, Martin (Chapter; Peer reviewed, 2011)The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention ... -
Ant colony optimisation-based classification using two-dimensional polygons
Goodwin, Morten; Yazidi, Anis (Peer reviewed; Journal article, 2016) -
“Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids
Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (Journal article; Peer reviewed, 2017)A Pattern Recognition (PR) system that does not involve labelled samples requires the clustering of the samples into their respective classes before the training and testing can be achieved. All of the reported clustering ... -
“Anti-Bayesian” Flat and Hierarchical Clustering Using Symmetric Quantiloids
Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (Chapter; Peer reviewed, 2016) -
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
Yazidi, Anis; Abolpour Mofrad, Asieh; Goodwin, Morten; Hammer, Hugo Lewi; Arntzen, Erik (Peer reviewed; Journal article, 2020) -
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
Astudillo, César A.; Gonzalez, Javier I.; Oommen, John; Yazidi, Anis (Chapter, 2016)In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called ... -
Deepfakes: current and future trends
Gambín, Ángel Fernández; Yazidi, Anis; Vasilakos, Athanasios; Haugerud, Hårek; Djenouri, Youcef (Peer reviewed; Journal article, 2024)Advances in Deep Learning (DL), Big Data and image processing have facilitated online disinformation spreading through Deepfakes. This entails severe threats including public opinion manipulation, geopolitical tensions, ... -
Distributed Learning Automata-based S-learning scheme for classification
Goodwin, Morten; Yazidi, Anis; Jonassen, Tore Møller (Peer reviewed; Journal article, 2019) -
Distributed learning automata-based scheme for classification using novel pursuit scheme
Goodwin, Morten; Yazidi, Anis (Journal article; Peer reviewed, 2020) -
Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm
Mohan, Ratish; Yazidi, Anis; Feng, Boning; Oommen, John (Chapter, 2016) -
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
Meng, Li; Yazidi, Anis; Goodwin, Morten; Engelstad, Paal (Peer reviewed; Journal article, 2022)In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims to incorporate semi-supervised learning into reinforcement ...