Vahid Seydi

Vahid Seydi
Bangor University · School of Ocean Sciences

Doctor of Philosophy

About

22
Publications
4,478
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
179
Citations
Introduction
Vahid Seydi is a Research Fellow in the School of Ocean Science at Bangor University in Data Science and Machine Learning. Prior to Bangor, he was an Assistant Professor at the Department of AI at Azad University South Tehran Branch. He received a B.Sc.(2005) in software engineering, Ms Sc. (2007) and PhD(2014) in AI, from the Department of Computer Science at Azad University, Science and Research Branch, Tehran Iran.

Publications

Publications (22)
Article
In order to estimate the Pareto front, most of the existing evolutionary algorithms apply the discovery of non-dominated solutions in search space, and most algorithms need appropriate diversity. Sometimes the Pareto front is so much thin and several dominated solutions exist beside the Pareto front. This paper proposes a new inverse model-based ev...
Article
Full-text available
Dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selecti...
Article
Full-text available
Twitter is one of the most popular and renowned online social networks spreading information which although dependable could lead to spreading improbable and misleading rumors causing irreversible damage to individuals and society. In the present paper, a novel approach for detecting rumor-based conversations of various world events such as real-wo...
Chapter
Full-text available
Domain adaptation is an attractive approach given the availability of a large amount of labeled data with similar properties but different domains. It is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adap...
Article
The basic idea in the estimation of distribution algorithms is the replacement of heuristic operators with machine learning models such as regression models, clustering models, or classification models. So, recently, the model‐based evolutionary algorithms (MBEAs) have been suggested in three groups: The estimation of distribution algorithms (EDAs)...
Article
Full-text available
With the increasing popularity of the social network Twitter and its use to propagate information, it is of vital importance to detect rumors prior to their dissemination on Twitter. In the present paper, a model to detect rumor conversations is proposed using graph convolutional networks. A reply tree and user graph were extracted for each convers...
Article
Full-text available
This paper presents the extraction of the emotional signals from traumatic brain-injured (TBI) patients through the analysis of facial features and implementation of the effective emotion-recognition model through the Pepper robot to assist in the rehabilitation process. The identification of emotional cues from TBI patients is very challenging due...
Article
Full-text available
Image captioning is a task to make an image description, which needs recognizing the important attributes and also their relationships in the image. This task requires to generate semantically and syntactically correct sentences. Most image captioning models are based on RNN and MLE methods, but we propose a novel model based on GAN networks where...
Article
Digital watermarking is a method for data hiding that ensures the security of multimedia data. The watermark can be a digital image or data stored within digital content. The Shearlet transform, a multi-resolution and multi-directional conversion, can be used for watermarking in digital images. Due to its superior features, this conversion can incr...
Article
Full-text available
Domain adaptation is one of the machine learning approaches, which is very powerful and applicable especially when there is no labeled data on the target domain or there are unequal distributions and different feature spaces between the source and target domains. This paper proposes an unsupervised domain adaptation model, which addresses this prob...
Article
Full-text available
Learning methods are challenged when there is not enough labeled data. It gets worse when the existing learning data have different distributions in different domains. To deal with such situations, deep unsupervised domain adaptation techniques have newly been widely used. This paper surveys such domain adaptation methods that have been used for cl...
Article
Most existing methods of multiobjective estimation of distributed algorithms apply the estimation of distribution of the Pareto‐solution on the decision space during the search and little work has proposed on making a regression‐model for representing the final solution set. Some inverse‐model‐based approaches were reported, such as inversed‐model...
Article
Recommender systems try to discover some latent features of users and items by looking at the available information such as users' history of ratings to items and then use these latent factors to estimate users' interest level in a particular item. Traditional methods such as standard matrix factorisation rely on the ratings that users have submitt...
Article
Recommender systems try to discover some latent features of users and items by looking at the available information such as users' history of ratings to items and then use these latent factors to estimate users' interest level in a particular item. Traditional methods such as standard matrix factorisation rely on the ratings that users have submitt...
Conference Paper
Cultural Algorithm is an evolutionary model inspired by the cultural evolution process which employs a basic set of knowledge sources, each related to knowledge observed in various social species. This study presents a modified version of cultural algorithm which benefits from adaptive fuzzy system. The adaptive fuzzy system is implemented as an ex...
Article
Full-text available
This study presents the normative knowledge source for the belief space of cultural algorithm(CA) based on an adaptive Radial Basis Function Neural Network (RBFNN). The use of the RBFNN makes it possible to use the previous upper and lower bounds of the normative knowledge to update them and to extract a logical relationship between the previous pa...
Conference Paper
Full-text available
The non-dominate sorting genetic algorithmic-II (NSGA-II) is a relatively recent technique for finding or approximating the Pareto-optimal set for multi-objective optimization problems. In different studies NSGA-II has shown good performance in comparison to other multi-objective evolutionary algorithms (Deb et al., 2002). In this paper an improved...
Article
Full-text available
Use of Multi-Objective Particle Swarm Optimization for designing of planar multilayered electromagnetic absorbers and finding optimal Pareto front is described. The achieved Pareto presents optimal possible trade-offs between thickness and reflection coefficient of absorbers. Particle swarm optimization method in comparison with most of optimizatio...
Conference Paper
Full-text available
This paper introduces a new approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO) with some modification in it to the training of al...
Conference Paper
Full-text available
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS).This approach based on multi objective optimization mechanism for training parameters in antecedent part. It considers two cost functions as the objectives which are the maximum difference measurements between the real nonlinear system...

Network

Cited By