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Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials

Neuroscience Informatics 2025年第1期5卷 100177-100177页

作者： Mohsenpourian, Moussa Suratgar, Amir Abolfazl Ali Talebi, Heidar Arzani, Mahsa Moghadasi, Abdorreza Naser Malakouti, Seyed Matin Bagher Menhaj, Mohammad Distributed and Intelligent Optimization Research Laboratory Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Real-time Robotics Laboratory Department of Electrical Engineering Amirkabir University of Technology Hafez Ave. Tehran Iran Department of Neurology Sian Hospital Tehran University of Medical Sciences Tehran Iran MS Research Center Sina Hospital Tehran University of Medical Sciences Hasan Abad Sq. Tehran Iran

In this study, a new approach for modification of membership functions of a fuzzy inference system (FIS) is demonstrated, in order to serve as a pattern recognition tool for classification of patients diagnosed with multiple sclerosis (MS) from healthy controls (HC) using their visually evoked potential (VEP) recordings. The new approach utilizes Krill Herd (KH) optimization algorithm to modify parameters associated with membership functions of both inputs and outputs of an initial Sugeno-type FIS, while making sure that the error corresponding to training of the network is minimized. This novel pattern recognition system is applied for classification of VEP signals in 11 MS patients and 11 HC's. A feature extraction routine was performed on the VEP signals, and later substantial features were selected in an optimized feature subset selection scheme employing Ant Colony optimization (ACO) and Simulated Annealing (SA) algorithms. This alone provided further information regarding clinical value of many previously unused VEP features as an aide for making the diagnosis. The newly designed computational intelligence system is shown to outperform popular classifiers (e.g., multilayer perceptron, support-vector machine, etc.) and was able to distinguish MS patients from HC's with an overall accuracy of 90%. © 2024 The Author(s)

关键词： Binary classification Fuzzy inference system Krill herd optimization Multiple sclerosis Visual evoked potentials

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Machine learning and transfer learning techniques for accurate brain tumor classification

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Clinical eHealth 2024年第1期7卷 106-119页

作者： Matin Malakouti, Seyed Bagher Menhaj, Mohammad Abolfazl Suratgar, Amir Distributed and Intelligent Optimization Research Laboratory Department of Electrical Engineering Amirkabir University of Technology

Brain tumors, resulting from uncontrolled and rapid cell growth, pose significant health risks if not treated early. Despite numerous advancements, accurate segmentation and classification remain challenging. This study leverages machine learning (ML) and transfer learning techniques to classify healthy and sick individuals using numerical data and MRI images. We utilized 3762 MRI images alongside Light Gradient Boosting Machine (LightGBM), AdaBoost, gradient boosting, Random Forest, Quadratic Discriminant Analysis, Linear Discriminant Analysis, logistic regression, and transfer learning algorithms. Numerical data was processed with LightGBM, achieving an accuracy of 95.7 %. Transfer learning applied to image data using a modified GoogLeNet model further enhanced classification accuracy to 99.3 %. These results demonstrate the effectiveness of combining ML and transfer learning techniques for accurate brain tumor classification, addressing limitations of prior approaches and offering improved diagnostic reliability. All coding and model implementations were conducted on the Python platform. © 2024

关键词： Brain tumor Light gradient boosting machine MRI images Numerical data Transfer learning

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Condition Monitoring of an Industrial Oil Pump Using a Learning Based Technique

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Sound & Vibration 2020年第4期54卷 257-267页

作者： Amin Ranjbar Amir Abolzafl Suratgar Saeed Shiry Ghidary Jafar Milimonfared Distributed and Intelligent Optimization Research Laboratory Department of Electrical EngineeringAmirkabir University of TechnologyTehran***Iran Department of Computer Engineering Amirkabir University of TechnologyTehran***Iran Department of Electrical Engineering Amirkabir University of TechnologyTehran***Iran

This paper proposes an efficient learning based approach to detect the faults of an industrial oil *** proposed method uses the wavelet transform and genetic algorithm(GA)ensemble for an optimal feature extraction *** features,which are dominated through this method,can remarkably represent the mechanical faults in the damaged *** the aim of condition monitoring,we considered five common types of malfunctions such as casing distortion,cavitation,looseness,misalignment,and unbalanced mass that occur during the machine *** proposed technique can determine optimal wavelet parameters and suitable statistical functions to exploit excellent features via an appropriate distance criterion ***,our optimization algorithm chooses the most appropriate feature submatrix to improve the final accuracy in an iterative *** a case study,the proposed algorithms are applied to experimental data gathered from an industrial heavy-duty oil pump installed in Arak Oil Refinery *** experimental results are very promising.

关键词： Condition monitoring fault assessment industrial pump genetic algorithm wavelet packet decomposition

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Solving Ac Optimal Power Flow(Ac-Opf) Consideringenergy Storage Dynamics Using distributed Gradientdynamics

SSRN

引用

SSRN 2024年

作者： Malakouti, Seyed Matin Shirazi, Mohammad Reza Suratgar, Amir Abolfazl Menhaj, Mohammad Bagher Distributed and Intelligent Optimization Research Laboratory Department of Electrical and Computer Engineering Amirkabir University of Technology Theran Iran

The seamless integration of distributed Energy Resources (DER) intoextensive power grids is a critical challenge in the modern energy sector,with significant implications for grid reliability, energy efficiency, and thetransition towards renewable energy sources. To address this, our researchfocuses on the effective and dependable utilization of Energy Storage Systems(ESS) for each bus in the grid. We propose reformulating the modified ACOptimal Power Flow (ACOPF) problem as a gradient dynamical system,considering the dynamics and inequality constraints associated with *** use a standard IEEE 14-bus system as a case study and present theresults for one-hour and 24-hour periods. Our findings offer valuable insightsfor enhancing the integration of DERs into power grids, contributing to theadvancement of smart grid technologies and the broader efforts towards amore sustainable and resilient energy infrastructure © 2024, The Authors. All rights reserved.

关键词： Renewable energy resources

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Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion

arXiv

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arXiv 2024年

作者： Janani, Pooya Suratgar, Amirabolfazl Taghvaeipour, Afshin Distributed and Intelligent Optimization Research Laboratory Dept. of Electrical Engineering Amirkabir University of Technology Tehran Iran Distributed and Intelligent Optimization Research Laboratory Dept. of Electrical Engineering Amirkabir University of Technology Tehran Iran Dept. of Mechanical Engineering Amirkabir University of Technology Tehran Iran

This paper proposes a hybrid fusion-based deep learning approach based on two different modalities, audio and video, to improve human activity recognition and violence detection in public places. To take advantage of audiovisual fusion, late fusion, intermediate fusion, and hybrid fusion-based deep learning (HFBDL) are used and compared. Since the objective is to detect and recognize human violence in public places, Real-life violence situation (RLVS) dataset is expanded and used. Simulating results of HFBDL show 96.67% accuracy on validation data, which is more accurate than the other state-of-the-art methods on this dataset. To showcase our model’s ability in real-world scenarios, another dataset of 54 sounded videos of both violent and non-violent situations was recorded. The model could successfully detect 52 out of 54 videos correctly. The proposed method shows a promising performance on real scenarios. Thus, it can be used for human action recognition and violence detection in public places for security *** Codes 68T07 (Primary), 68T45, 62H30 (Secondary) © 2024, CC BY-NC-SA.

关键词： Audiovisual

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distributed and Decentralized optimization with Unknown Agents: A Virtual Power Plant Scheduling Application

SSRN

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SSRN 2023年

作者： Gholami, Amir Hossein Suratgar, Amir Abolfazl Menhaj, Mohammad Bagher Hesamzadeh, Mohammad Reza Distributed Intelligent Optimization Research Laboratory Electrical Engineering Department Amirkabir University of Technology Tehran Iran Electrical Engineering Department KTH Royal Institute of Technology Stockholm Sweden

Due to optimization of expanded networks as power grids and communication networks, using centralized optimization methods faces some challenges including computation burden. To overcome the problem of the centralized optimization method, decentralized and distributed optimization methods have been proposed. This paper fully considers this problem and then proposes a distributed-decentralized bi-level optimization algorithm to resolve it. In the first level of the proposed bi-level algorithm, the distributed penalty primal-dual sub-gradient (DPPDS) algorithm, which is a distributed algorithm, is used to optimize a set of bundles. Available information in each bundle comes only from its neighbors. In the second level, alternating direction multipliers of methods (ADMM) algorithm is used to optimize within bundle agents. The ADMM algorithm is a widely used decentralized algorithm. Proof of convergence of each optimization level is fully investigated. The convergence of integrated bi-level optimization is guaranteed. The proposed method, distributed-decentralized bi-level (DDB) optimization algorithm is applied in virtual power plants (VPP). As a case study, six VPPs are considered and one of them is considered as an agent with unknown dynamics. Adaptive neural-fuzzy inference system (ANFIS) is used to identify the unknown agent. The proposed method is finally compared with the bi-level linear programming (BLP) reformulation algorithm. The simulation results are promising. © 2023, The Authors. All rights reserved.

关键词： Linear programming

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Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Malakouti, Seyed Matin Karimi, Farrokh Abdollahi, Hamid Menhaj, Mohammad Bagher Suratgar, Amir Abolfazl Moradi, Mohammad Hassan Distributed and Intelligent Optimization Research Laboratory Department of Electrical Engineering Amirkabir University of Technology Research Fellow Institute for Research in Fundamental Sciences (IPM) Sharif University of Technology Department of Biomedical Engineering BioelectricAmirkabir University of Technology

Due to the shortage of fossil fuels in many countries, power plants that rely on fossil fuels will be phased out in favor of wind turbines as the primary source of energy generation. These fossil fuel plants wreak havoc on the natural world, making humans and other life forms susceptible to illness. The production potential of wind turbines was investigated. Consequently, methods such as XGBOOST, Multi-Layer Perceptron with Bayesian optimization (MLP + BO), Gradient Boosting Regression Tree (GBDT), Ensemble (gradient boosting and xgboost), and CNN Long Short-Term Memory (CNN-LSTM) have been utilized. A mean square error (MSE) of 7.2 in 45 seconds was achieved using the Ensemble technique, and an MSE of 6.8 in 450 seconds was obtained with the CNN-LSTM method. Wind power is readily available and straightforward to acquire globally, indicating its potential as a reliable and sustainable energy source. © 2024 The Author(s)

关键词： CNN-LSTM method Ensemble (gradient boosting and xgboost) Multi-layer Perceptron with bayesian optimization Wind turbines

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Enhancing Cancer Detection Through DNA Sequences and Ensemble Method

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intelligent Systems with Applications 2024年

作者： Seyed Matin Malakouti Arvin Esfandiari Mohammad Bagher Menhaj Amir Abolfazl Suratgar Ali Doustmohammadi Distributed and Intelligent Optimization Research Laboratory Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

In the realm of cancer research, machine learning algorithms have emerged as robust tools for analyzing DNA sequences, a critical aspect for early detection and risk assessment. Despite notable advancements in this domain, there exists a persistent demand for a predictive model that demonstrates high accuracy in estimating cancer risk. This study endeavors to address this exigency by employing an array of classification algorithms, including Logistic Regression, Gradient Boosting, Gaussian Naive Bayes, and a Blending method that amalgamates Logistic Regression and Gaussian Naive Bayes. These algorithms are fine-tuned with hyperparameters through Grid search techniques to predict cancer occurrences within a cohort of 390 individuals with characterized DNA sequences. The Blending method exhibits superior predictive performance in discerning five specific types of cancer: BRCA1 (Breast Cancer gene 1), KIRC-2 (Kidney Renal Clear Cell Carcinoma), COAD-3 (Colorectal Adenocarcinoma), LUAD-4 (Lung Adenocarcinoma), and PRAD-5 (Prostate Adenocarcinoma), achieving accuracy rates ranging from 96% to 100%. Notably, it significantly surpasses individual algorithms in predicting LUAD-4 and PRAD-5, with the Blending technique (incorporating Logistic Regression and Gaussian Naive Bayes) attaining an accuracy of 98%. The magnitude of this enhancement is manifest in the Micro-average and Macro-average ROC curves, which ascend to 99%. These findings underscore the potential of the Blending method as a valuable asset in cancer research, presenting promising prospects for enhanced accuracy and efficacy in cancer prediction endeavors.

关键词： DNA sequences Cancer Machine learning Blending method

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