Books by Seyed Muhammad Hossein Mousavi
arXiv, 2023
GitHub Repository: https://github.com/SeyedMuhammadHosseinMousavi/Introduction-to-Facial-Micro-Ex... more GitHub Repository: https://github.com/SeyedMuhammadHosseinMousavi/Introduction-to-Facial-Micro-Expressions-Analysis-Using-Color-and-Depth-Images-a-Matlab-Coding-Appr The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artificial Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. My goal is to provide a standalone introduction in the field of FMER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible MATLAB practical examples. Also, the book describes any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. This book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. It is expected that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro expressions recognition, feature extraction and dimensionality reduction. This book is product of several years of researches and experiments and reflects the mindset of the authors for understanding this field as easier as possible. The author encourages the reader to contact him with any comments and suggestions for improvement.
Papers by Seyed Muhammad Hossein Mousavi
MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset, 2025
Automatic emotion recognition gained significant importance in the recent decade, especially with... more Automatic emotion recognition gained significant importance in the recent decade, especially with the development of artificial intelligence, which has affected our daily lives. Using personalized emotion recognition in healthcare, education, retail, and automotive has high importance these days, which requires proper data in different modalities. On the other hand, data scarcity in some emotion recognition modalities, such as body motion and physiological signals, is vivid, especially when it comes to multimodality. Furthermore, the way a participant is provoked to show emotion is crucial, as it should resemble real-life emotional expression. To do so, one of the most effective methods is to employ Virtual Reality (VR) videos and games. This paper introduces a novel Multimodal Virtual Reality Stimuli-based emotion recognition dataset, or MVRS, which could address the mentioned data scarcity issue. Our dataset contains 13 subjects or participant stimuli using VR videos for relaxation, fear, stress, sadness, and joy emotions. The dataset covers an age range of 12 to 60 in both genders. This dataset is recorded in a small lab in which all participants followed the same data collection protocols and filled out both questionnaires and consent forms. The dataset includes eye tracking, body motion, ElectroMyoGraphy (EMG), and Galvanic Skin Response (GSR) data in various formats. The eye-tracking data is recorded using a Full High-Definition (FHD) webcam placed manually into the VR Head-Mounted Display (HMD). The body motion data is recorded using Microsoft Kinect version 2. Finally, EMG and GSR data are recorded by an Arduino UNO board. All data is recorded simultaneously, with synchronized timestamps, to support clean data for multimodal processing. For each modality, related features are extracted and fused by multimodal fusion techniques (early and late stages) and evaluated using different classifiers and metrics to check the validity and separability of the data.
As Artificial General Intelligence (AGI) advances toward self-awareness, critical ethical and phi... more As Artificial General Intelligence (AGI) advances toward self-awareness, critical ethical and philosophical questions emerge regarding its consciousness, personhood, and moral status. If an AGI exhibits self-awareness and cognitive reasoning, does it possess a soul or deserve ethical considerations similar to sentient beings? This paper investigates these concerns, particularly focusing on the ethics of deleting AGI datasets, which may be similar to erasing a living entity. Addressing these profound uncertainties requires a systematic and interpretable approach, which we achieve through fuzzy logic and machine learning-based ethical classification. We employ fuzzy logic to model ethical ambiguity, allowing for a continuous ethicality spectrum rather than rigid binary classifications. Additionally, XGBoost, a state-of-the-art classification model, is used to assess ethicality, achieving 91.66% accuracy and validating the feasibility of AI-driven ethical assessment. To cover transparency in decision-making, we used Explainable AI (XAI) techniques, including SHAP and feature importance analysis, revealing that moral implications exert the strongest influence on ethical classification, followed by cognitive abilities and self-awareness. The importance of this study lies in its challenge to traditional ethical paradigms, highlighting the urgent need to redefine AI governance frameworks and address whether AGI deserves ethical protections. The results suggest that deleting AGI data may not be an ethically neutral act, reinforcing the need for accountable and transparent AI policies. By bridging AI ethics, machine learning, and explainability, this research contributes to the ongoing discourse on the moral responsibilities of AGI creators and the broader implications of conscious AI systems in society.
Journal of Future Sustainability
Weevils are a type of insect with elongated snouts coming from superfamily of Curculionoidea with... more Weevils are a type of insect with elongated snouts coming from superfamily of Curculionoidea with approximately 97,000 species. Most of them consider pest and cause environmental damages but some kinds like wheat weevil, maize weevil, and boll weevils are famous to cause huge damage on crops, especially cereal grains. This research proposes a novel swarm-based metaheuristics algorithm called Weevil Damage Optimization Algorithm (WDOA) which mimics weevils’ fly power, snout power, and damage power on crops or agricultural products. The proposed algorithm is tested with 12 benchmark unimodal and multimodal artificial landscapes or optimization test functions. Additionally, the proposed WDOA is employed in five engineering problems to check its robustness for problem solving. Problems are Travelling Salesman Problem (TSP), n-Queens problem, portfolio problem, Optimal Inventory Control (OIC) problem, and Bin Packing Problem (BPP). All tests’ functions are compared with widely used bench...
Introducing Bee-Eater Hunting Strategy Algorithm for IoT-Based Green House Monitoring and Analysis
From data to action: Empowering COVID-19 monitoring and forecasting with intelligent algorithms
Journal of the Operational Research Society
arXiv (Cornell University), Jan 22, 2023
The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spher... more The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and creates a large shadow underneath, killing any plants that require sunlight. These water tyrants use their formidable spines to compel each other to the surface and increase their strength to grab more space from the surface. As they spread throughout the pond or basin, with the earliest-growing leaves having more room to grow, each leaf gains a unique size. Its flowers are transsexual and when they bloom, Cyclocephala beetles are responsible for the pollination process, being attracted to the scent of the female flower. After entering the flower, the beetle becomes covered with pollen and transfers it to another flower for fertilization. After the beetle leaves, the flower turns into a male and changes color from white to pink. The male flower dies and sinks into the water, releasing its seed to help create a new generation. In this paper, the mathematical life cycle of this magnificent plant is introduced, and each leaf and blossom are treated as a single entity. The proposed bio-inspired algorithm is tested with 24 benchmark optimization test functions, such as Ackley, and compared to ten other famous algorithms, including the Genetic Algorithm. The proposed algorithm is tested on 10 optimization problems:
Nature-Inspired DMU Selection and Evaluation in Data Envelopment Analysis
Lecture notes on data engineering and communications technologies, 2023
Journal of Future Sustainability, 2023
One of the most hazardous phenomena in forests is wildfire or bush fire and early detection of ma... more One of the most hazardous phenomena in forests is wildfire or bush fire and early detection of massive damage prevention is vital. Employing Unmanned Aerial Vehicles (UAV) as a visual and extinguisher tool in order to prevent this tragedy which brings fatal effects on humans and wildlife has high importance. Additionally, using aerial imagery could assist firefighters to recognize fire intensity and localize and route the fire in the forest which shrinks down casualties of firefighters. All these benefits and more is just possible by employing cheap UAVs. The proposed research uses nature-inspired image processing techniques in order to segment and classify fire in color and thermal images. Multiple nature-inspired and traditional computer vision techniques, including Chicken Swarm Algorithm (CSA) intensity adjustment (contrast enhancement), Denoising Convolutional Neural Network (DnCNN), Local Phase Quantization (LPQ) feature extraction, Bees Image Segmentation, Biogeography-Based Optimization (BBO) feature selection, Firefly Algorithm (FA) classification and more are employed to achieve high classification and segmentation accuracy. The system evaluates nine performance metrics including, F-Score, Accuracy, and Jaccard for the segmentation stage and four performance metrics for the classification stage. All experiments are conducted on the two most recent UAV fire datasets of FLAME (2021) and DeepFire (2022). Additionally, fire intensity, fire direction, and fire geometrical calculation are calculated which assists firefighters even more. As smoke shows the location of the fire, a smoke detection workflow is proposed, too. Proposed system Compared with traditional and novel methods for segmentation and classification leading to satisfactory and promising results for almost all metrics. The trained model of this system could be used in most of the current rescue UAVs in real-time applications. For the FLAME dataset (color data), segmentation precision is 95.57 % and classification accuracy is 91.33 %. Also, For the DeepFire dataset segmentation precision is 91.74 % and classification accuracy is 96.88 %.
Introducing Bee-Eater Hunting Strategy Algorithm for IoT-Based Green House Monitoring and Analysis
2022 Sixth International Conference on Smart Cities, Internet of Things and Applications (SCIoT)
Simulated Annealing Edge Detection
Recognizing, Distinguishing and Tracking Enemy Army by Missile's RGB-D Sensors, to Decrease Civilian's Casualty, in Missile War
Testing Optimization Algorithms Easier with a New Test Function for Single-Optimization Problems and Validating Evolutionary Algorithms
Differential Evolution Clustering
Human face states the inner emotions, thoughts and physical disorders. These emotions are express... more Human face states the inner emotions, thoughts and physical disorders. These emotions are expressed on the face via facial muscles. The estimated time through which a facial expression occurs on the face is between 0.5 to 4 seconds, and a micro expression between 0.1 to 0.5 seconds. Obviously, for the purpose of recording micro expressions, obtaining videos frames between 30 up to 200 frame per second is essential. This research uses Kinect V.2 sensor to get the color and depth data in 30 fps. Depth image stores useful 2.5-Dimentional information from skin wrinkles which is the main key to recognize even slightest micro facial expressions. Experiment starts with splitting color and depth images into facial parts, and after applying preprocessing techniques, features extraction out of both type of data in spatial and frequency domain takes place. Some of the features which are used in this study are Histogram of Oriented Gradient (HOG), Gabor Filter, Speeded Up Robust Features (SURF)...
An Edge Detection System for Polluted Images by Gaussian, Salt and Pepper, Poisson and Speckle Noises
Due to the problems which noises may cause in receiving, transmission and processing images, and ... more Due to the problems which noises may cause in receiving, transmission and processing images, and the problems that traditional edge recognition methods such as: Canny, Zerocreoss, Log, Roberts, Prewitt and Sobel have in relation to these noises, especially Gaussian, Salt and Pepper, Poisson and Speckle, we decided to design a system which is not only an edge detection method, but in the case, facing different noises, is able to indicate least of sensitivity. This system comprises three section, which initially divide the RGB image to its constituent colours, and then applies a filter to them in order to make it smooth (without changing the real position of the edges). In the next section, a new filter is employed to display the edges, and in the last section, a post-process will takes place on the output binary image for eliminating superfluous spots. The acquired results of comparison of this system with traditional edge detection operators indicates the high efficiency and robustness of it, against different noises. Also in order to statistically validation, we benefit from PSNR, MSE and SSIM factors. Even statistical results indicate the high similarity of noisy edge detected images and non-noisy edge detected images in the proposed system.
Fingerprint Recognition Out of Object's Surface Using Ordinary Cameras with the Aid of Magnifier
New Artificial Landscape for Single-Objective Problems and Validation of Evolutionary Algorithms
In general terms, optimization means to improve the state of something; Having a function f(x) in... more In general terms, optimization means to improve the state of something; Having a function f(x) in optimization, we want to find an argument x whose relevant cost is optimum (usually minimum). The Test Functions, which known as Artificial Landscapes in Applied Mathematic, are utilized for assessing features of optimization algorithms such: convergence speed, accuracy, robustness and their total functionality. Single-objective optimization algorithms are the underlying basis to more sophisticated algorithms such: multi-objective optimization algorithms, niching, constrained optimization algorithms, etc. These functions are also used for testing of evolutionary algorithms (generally optimization). The aim of presenting this paper is to implement a sort of Test Function for single-objective optimization problems and validating of evolutionary algorithms. Acquired results indicate that, the proposed test function is able to perform optimization operation and validating evolutionary algorithms well.
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Books by Seyed Muhammad Hossein Mousavi
Papers by Seyed Muhammad Hossein Mousavi