A5093527492- Brain Tumor Detection and Classification
- Cell Image Analysis Techniques
- Poxvirus research and outbreaks
- IoT and Edge/Fog Computing
- Advanced Neural Network Applications
- Artificial Intelligence in Healthcare
- Fire Detection and Safety Systems
- Radiomics and Machine Learning in Medical Imaging
- Blockchain Technology Applications and Security
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- Transportation and Mobility Innovations
- Fault Detection and Control Systems
- Medical Imaging and Analysis
- Quantum Computing Algorithms and Architecture
- Privacy-Preserving Technologies in Data
- AI in cancer detection
- Digital Media Forensic Detection
- Fire effects on ecosystems
- Liver Disease Diagnosis and Treatment
- IoT-based Smart Home Systems
- Heart Rate Variability and Autonomic Control
- Online Learning and Analytics
- Infant Health and Development
- EEG and Brain-Computer Interfaces
Khulna University
2019-2025
Taif University
2024
Deakin University
2024
Chittagong University of Engineering & Technology
2024
In the wake of COVID-19, rising monkeypox cases pose a potential pandemic threat. While less severe than its increasing spread underscores urgency early detection and isolation to control disease. The main difficulty in diagnosing arises from prolonged diagnostic process symptoms that are similar those other skin diseases, making challenging. To address this, deployment deep learning models on edge devices presents viable solution for rapid accurate monkeypox. However, resource constraints...
Diagnosing brain tumors using magnetic resonance imaging (MRI) presents significant challenges due to the complexities of segmentation and variability in tumor characteristics. To address limitations inherent traditional methods, this research employs an advanced deep learning approach, integrating ResNet50 for feature extraction Generative Adversarial Networks (GANs) data augmentation. A comprehensive evaluation ten transfer algorithms, including GoogLeNet VGG-16, was conducted...
Hepatitis is a widespread inflammatory condition of the liver, presenting formidable global health challenge. Accurate and timely detection hepatitis crucial for effective patient management, yet existing methods exhibit limitations that underscore need innovative approaches. Early-stage now possible with recent adoption machine learning deep With this in mind, study investigates use traditional models, specifically classifiers such as logistic regression, support vector machines (SVM),...
Scheduling of robots is one the imperative assignment in a multi robot system. prerequisite when there multiple task need to be assigned an arranged manner. There growing for perform complex tasks autonomously. Multi-robot environment becomes as are factors addressed simultaneously which require fast computation and more space. Using cloud computing platform could optimal solution this problem. This paper presents use implementing proposed Periodic Min-Max Algorithm (PMW) scheduling. Amazon...
<abstract> <p>This study presented a new approach to seizure classification utilizing electroencephalogram (EEG) data. We introduced the NeuroWave-Net, an innovative hybrid model that seamlessly integrates convolutional neural networks (CNN) and long short-term memory (LSTM) architectures. Unlike conventional methods, our capitalized on CNN's proficiency in feature extraction LSTM's prowess classifying seizure. The key strength of NeuroWave-Net lies its ability combine these...
Viruses are submicroscopic agents that can infect other lifeforms and use their hosts' cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses highly adaptable, resilient, capable of causing severe complications in bodies. Due multiple transmission pathways, high contagion rate, lethality, pose the biggest biological threat both animal plant species face. It is often challenging promptly detect a virus host accurately determine its type...
IoT (Internet of Things) is dominating all over the world for developing technology. It another information industry following computer, internet, and mobile connection. In modern society, we must ensure security leading a comfortable life. Nowadays, has been affected by different types matters. Gas leakage fire incidents are considered among them. At present, there many undesirable accidents from gas incidents. One way to prevent involving incident detection affix device at adequate places....
Wildfires are a recurring concern in several regions around the globe, often leading to catastrophic outcomes. The increasing accessibility of small satellites operating Low Earth Orbit (LEO) has led potential for enhancing speed wildfire detection b y utilizing constellations m any miniature areas interest. This research presents system model detecting and reporting wildfires fire stations using LEO satellites. In this paper, we propose transfer learning-based lightweight deep learning at...
This paper introduces a method for flow monitoring through various supply lines by implementing mathematical function, majority of which is dependent on standard deviation. Instead including sensors and numerous complex electrical connections still the common way to measure monitor leakage or flow, this project just uses measuring sensor collect data process in such that actually focuses user conditions takes regular variation into account modifies its protocols regularly. The other systems...
Abstract Smart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of security privacy, combining distributed federated learning (FL) with blockchain technology is being considered prevent single point failure model poising attacks. However, adding a FL environment can worsen blockchain's scaling issues create regular interruptions at SH. This article presents scalable...