A5057338017- Medical Image Segmentation Techniques
- COVID-19 diagnosis using AI
- Brain Tumor Detection and Classification
- Radiomics and Machine Learning in Medical Imaging
- Blockchain Technology Applications and Security
- AI in cancer detection
- Digital Imaging for Blood Diseases
- Dementia and Cognitive Impairment Research
- Artificial Intelligence in Healthcare and Education
- Mathematical Biology Tumor Growth
- Supply Chain Resilience and Risk Management
- Visual Attention and Saliency Detection
- Distributed Control Multi-Agent Systems
- Cognitive Science and Mapping
- Metaheuristic Optimization Algorithms Research
- Big Data Technologies and Applications
- Microfinance and Financial Inclusion
- Intelligent Tutoring Systems and Adaptive Learning
- Supply Chain and Inventory Management
- Modular Robots and Swarm Intelligence
- COVID-19 impact on air quality
- IoT and Edge/Fog Computing
- Distributed and Parallel Computing Systems
- Machine Learning in Healthcare
- Anomaly Detection Techniques and Applications
École Supérieure du Commerce et des Affaires
2023-2024
Université Ibn-Tofail
2024
Cadi Ayyad University
2016-2023
Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there still numerous problems that need to solve. Thus, the advanced methods deploy artificial intelligence (AI) necessary. The use of cooperative agents in increases efficiency automatic image segmentation. Hence, we introduce a new method is multi-agent deep reinforcement learning (DRL) minimize long-term manual and enhance medical segmentation frameworks. A DRL-based introduced deal with...
The learning techniques have a particular need especially for the detection of invisible brain diseases. Learning-based methods rely on MRI medical images to reconstruct solution detecting aberrant values or areas in human brain. In this article, we present method that automatically performs segmentation detect damage and diagnose Alzheimer's disease (AD). order take advantages benefits 3D reduce complexity computational costs, 2.5D locating inflammation their classes. Our proposed system is...
IntroductionWith the increasing number of Covid-19 cases as well care costs, chest diseases have gained interest in several communities, particularly medical and computer vision. Clinical analytical exams are widely recognized techniques for diagnosing handling cases. However, strong detection tools can help avoid damage to tissues. The proposed method provides an important way enhance semantic segmentation process using combined potential deep learning (DL) modules increase consistency....
Abstract Since their beginning, Massive Open Online Courses (MOOC) have known great success and managed to establish themselves with significant enrollment rates. However, this was quickly disrupted by the drop‐out phenomenon observed in majority of MOOCs, which reaches 90% some courses. Studying understanding phenomenon, consequently determining relevance efforts made develop has led several researchers propose predictive models learners at risk dropping out. On one hand, these been relying...
Multi-agent technology has been considered as an important approach for developing distributed intelligent systems analyzing computed tomography (CT). Due to the interactions, multi-agent problem complexity can rise rapidly with number of agents or their behavior. We present a MAS solution that spawned increasing interest in machine techniques automate search and optimization image processing. In this survey we propose three dimensional (3D) segmentation process based on cooperation between...
It is generally accepted that segmentation a critical problem influences subsequent tasks during image processing. Often, the proposed approaches provide effectiveness for limited type of images with significant lack global solution. The difficulty lies in complexity providing solution acceptable accuracy within reasonable time. To overcome this problem, some solutions combined several methods. This paper presents method segmenting 2D/3D by merging regions and solving problems encountered...
In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes cooperative method relying on Multi-Agent System. The main contribution of work is to highlight importance between contour and region growing based System (MAS). Consequently, agents’ interactions form part whole process segmentation. Similar works were proposed evaluate effectiveness solution. difference that our can perform segmentation ensuring efficiency. Our...
ABSTRACT The global impact of COVID‐19 has resulted in millions individuals being afflicted, with a staggering mortality toll over 16 000 span 2 years. dearth resources and diagnostic techniques had an on both emerging wealthy nations. In response to this, researchers from the domains engineering medicine are using deep learning methods create automated algorithms for detecting COVID‐19. This work included development comparison collaborative deep‐learning model identification CT scan...
This paper presents an advanced Alzheimer's disease (AD) diagnosis by ensuring cooperative segmentation based on a powerful multi-agent system (MAS). approach builds the strengths of MAS highlighting importance cooperation between methods used and strong capacity agents to negotiate, resolve cases ambiguity make decisions adopting Pareto optimal (PO) game theory. The results obtained demonstrate reliability effectiveness our method.
Abstract With the increasing number of covid-19 as well care costs, chest diseases have gained interest in several communities, particularly medical fields and computer vision. Clinical analytical exams are widely recognized techniques for supervising COVID-19 cases. However, strong detection tools can help avoid damage to tissues. Interpreting images is a time-consuming assignment with great error level. Recently, advanced deep learning systems effectively proved superior performance...