A5049763844- IoT and Edge/Fog Computing
- Context-Aware Activity Recognition Systems
- Information and Cyber Security
- COVID-19 diagnosis using AI
- Advanced Malware Detection Techniques
- Artificial Intelligence in Healthcare
- Network Security and Intrusion Detection
- Machine Learning in Healthcare
- Digital Imaging for Blood Diseases
- Cloud Computing and Resource Management
- IoT Networks and Protocols
- IoT and GPS-based Vehicle Safety Systems
- Fire Detection and Safety Systems
- Vehicular Ad Hoc Networks (VANETs)
- Advanced Neural Network Applications
- Data Mining Algorithms and Applications
- AI in cancer detection
- Image Enhancement Techniques
- Vehicle License Plate Recognition
- Smart Cities and Technologies
- Advanced Image Processing Techniques
- Power Systems and Technologies
- Advanced Image Fusion Techniques
- Age of Information Optimization
- Advanced Algorithms and Applications
Technical University of Malaysia Malacca
2018-2023
Ministry of Higher Education and Scientific Research
2018-2023
Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around world. Machine learning (ML) one intelligent technique able automatically predict event with reasonable accuracy based on experience and process. In meantime, rapid number ML models have been proposed for predicate cases COVID-19. Thus, there need an evaluation benchmarking COVID-19 which main challenge this study....
In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus critical tasks. Their management at edge network can be done by Fog computing implementation. However, Nodes suffer from lake resources That could limit time needed for final outcome/analytics. perform just a small number A difficult decision concerns tasks will locally Nodes. Each node should select such carefully based on current contextual information, example, tasks’ priority,...
The quick spread of the Coronavirus Disease (COVID-19) infection around world considered a real danger for global health. biological structure and symptoms COVID-19 are similar to other viral chest maladies, which makes it challenging big issue improve approaches efficient identification disease. In this study, an automatic prediction is proposed automatically discriminate between healthy infected subjects in X-ray images using two successful moderns traditional machine learning methods...
Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts blood the entire body. Diagnosing occurrence early and efficiently may prevent manifestation debilitating effects this aid in its effective treatment. Classical methods for diagnosing are sometimes unreliable insufficient analyzing related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) provide reliable diagnosis...
In the last decade, developments in healthcare technologies have been increasing progressively practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers a manner called cloud computing. The emerging paradigm offers different models, fog computing edge computing, to enhance performances of minimum end-to-end delay network. However, many research challenges exist fog-cloud enabled network for applications. Therefore,...
COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon possible so that services and treatment can deployed intensified. Many biomarkers are being investigated in order to track patient's condition. Unfortunately, this may interfere with symptoms of other diseases, making it more difficult for a specialist diagnose or predict severity level case. This research develops Smart Healthcare System Severity Prediction Critical Tasks Management...
Abstract Images captured through a visual sensory system are degraded in foggy scene, which negatively influences recognition, tracking, and detection of targets. Efficient tools needed to detect, pre‐process, enhance scenes. Machine learning (ML) has significant role image defogging domain for tackling adverse issues. Unfortunately, regardless contributions that were made by ML, little attention been attributed this topic. This paper summarizes the ML methods relevant aspects research area....
Context: Security issues have increased recently because of the use networking. The researchers proposed many models, approaches, and for example, attack graphs. graph model is a valuable tool vulnerability analysis as well displaying all network paths. In general, graphs can be utilized variety purposes, including calculation security metrics. Nonetheless, in order to sufficiently safeguard networks, technique gauging degree provided by these activities required, “you cannot improve what...
The use of network technologies has increased in recent years. Although the is beneficial for individuals to work and live in, it does have security challenges that should be rectified. One these issues cyberattacks. attack surface hackers growing as more devices are linked internet. next-generation cyber defense concentrating on predictive analysis seems proactive than existing based intrusion detection. Recently, many approaches been proposed detect predict attacks; one graphs. main reason...
Abstract Recently, the oil and gas industry faced several crucial challenges affecting global energy market, including Covid-19 outbreak, fluctuations in prices with considerable uncertainty, dramatically increased environmental regulations, digital cybersecurity challenges. Therefore, industrial internet of things (IIoT) may provide needed hybrid cloud fog computing to analyze huge amounts sensitive data from sensors actuators monitor rigs wells closely, thereby better controlling...