21 citations · 24 across the 19 of their papers we have counts for
19 papers
Swarm and Evolutionary Computation for Near-Field Localization
Parisa Ramezani, Seyed Jalaleddin Mousavirad, Mattias O'Nils +1
Near-field localization has attracted significant attention in recent years due to the move toward higher frequencies and extremely large aperture arrays, which expand the near-fie…
CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging
Marianthi Adamopoulou, Parthasaarathy Sudarsanam, David Diaz-Guerra +5
Acoustic imaging visualization is a core methodology in acoustics, enabling spatial analysis of sound sources and acoustic scenes. However, limited sensor availability in practical…
A Primer on Evolutionary Optimization Frameworks for Near-Field Multi-Source Localization
Seyed Jalaleddin Mousavirad, Parisa Ramezani, Mattias O'Nils +1
This paper introduces evolutionary optimization as a grid-free training-free continuous-domain search mechanism for near-field multi-source localization, addressing the major limit…
Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios
Parisa Ramezani, Seyed Jalaleddin Mousavirad, Mattias O'Nils +1
The conventional MUltiple SIgnal Classification (MUSIC) algorithm is effective for angle-of-arrival estimation in the far-field and can be extended for full source localization in…
Robust Energy Consumption Prediction with a Missing Value-Resilient Metaheuristic-based Neural Network in Mobile App Development
Seyed Jalaleddin Mousavirad, Luís A. Alexandre
Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users. Main objective of this research i…
A Metaheuristic-based Machine Learning Approach for Energy Prediction in Mobile App Development
Seyed Jalaleddin Mousavirad, Luís A. Alexandre
Energy consumption plays a vital role in mobile App development for developers and end-users, and it is considered one of the most crucial factors for purchasing a smartphone. In a…
A Novel Plagiarism Detection Approach Combining BERT-based Word Embedding, Attention-based LSTMs and an Improved Differential Evolution Algorithm
Seyed Vahid Moravvej, Seyed Jalaleddin Mousavirad, Diego Oliva +1
Detecting plagiarism involves finding similar items in two different sources. In this article, we propose a novel method for detecting plagiarism that is based on attention mechani…
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification
Mehran Pourvahab, Seyed Jalaleddin Mousavirad, Virginie Felizardo +6
Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, parti…
Metaheuristic-based Energy-aware Image Compression for Mobile App Development
Seyed Jalaleddin Mousavirad, Luís A Alexandre
The JPEG standard is widely used in different image processing applications. One of the main components of the JPEG standard is the quantisation table (QT) since it plays a vital r…
Energy-Aware JPEG Image Compression: A Multi-Objective Approach
Seyed Jalaleddin Mousavirad, Luís A. Alexandre
Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. While different images with…
Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
Mahshid Helali Moghadam, Markus Borg, Mehrdad Saadatmand +3
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural netw…
HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin +3
The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation pro…
MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm
Ehsan Bojnordi, Seyed Jalaleddin Mousavirad, Gerald Schaefer +1
Population-based metaheuristic algorithms have received significant attention in global optimisation. Human Mental Search (HMS) is a relatively recent population-based metaheuristi…
An LSTM-based Plagiarism Detection via Attention Mechanism and a Population-based Approach for Pre-Training Parameters with imbalanced Classes
Seyed Vahid Moravvej, Seyed Jalaleddin Mousavirad, Mahshid Helali Moghadam +1
Plagiarism is one of the leading problems in academic and industrial environments, which its goal is to find the similar items in a typical document or source code. This paper prop…
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin +3
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the m…
Ensemble of Loss Functions to Improve Generalizability of Deep Metric Learning methods
Davood Zabihzadeh, Zahraa Alitbi, Seyed Jalaleddin Mousavirad
Deep Metric Learning (DML) learns a non-linear semantic embedding from input data that brings similar pairs together while keeping dissimilar data away from each other. To this end…
Differential Evolution-based Neural Network Training Incorporating a Centroid-based Strategy and Dynamic Opposition-based Learning
Seyed Jalaleddin Mousavirad, Diego Oliva, Salvador Hinojosa +1
Training multi-layer neural networks (MLNNs), a challenging task, involves finding appropriate weights and biases. MLNN training is important since the performance of MLNNs is main…
Low-Rank Robust Online Distance/Similarity Learning based on the Rescaled Hinge Loss
Davood Zabihzadeh, Amar Tuama, Ali Karami-Mollaee
An important challenge in metric learning is scalability to both size and dimension of input data. Online metric learning algorithms are proposed to address this challenge. Existin…
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm
Shahryar Rahnamayan, Seyed Jalaleddin Mousavirad
Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of ma…