A5048357267- COVID-19 diagnosis using AI
- Smart Agriculture and AI
- AI in cancer detection
- Insect Utilization and Effects
- Text and Document Classification Technologies
- Digital Imaging for Blood Diseases
- Hate Speech and Cyberbullying Detection
- Spam and Phishing Detection
- Advanced Text Analysis Techniques
- IoT and Edge/Fog Computing
- Currency Recognition and Detection
- Solar and Space Plasma Dynamics
- Robot Manipulation and Learning
- Cybercrime and Law Enforcement Studies
- Non-Invasive Vital Sign Monitoring
- Radiomics and Machine Learning in Medical Imaging
- Adaptive Dynamic Programming Control
- Virtual Reality Applications and Impacts
- Impact of AI and Big Data on Business and Society
- Hand Gesture Recognition Systems
- Context-Aware Activity Recognition Systems
- Handwritten Text Recognition Techniques
- Face recognition and analysis
- Artificial Intelligence in Healthcare
- Digital Transformation in Industry
Jeonbuk National University
2024
University of Mysore
2021-2024
Kyung Hee University
2022-2024
Indian Institute of Information Technology Allahabad
2021-2023
Government Medical College
2023
Indian Institute of Information Technology and Management, Kerala
2021-2022
Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease’s impact on population, time series prediction models are necessary. The conventional technique of detecting disease for certified technicians to examine blood smears visually parasite-infected RBC (red cells) underneath a microscope. This procedure ineffective, diagnosis depends individual performing test his/her experience. Automatic image...
Blood cells carry important information that can be used to represent a person's current state of health. The identification different types blood in timely and precise manner is essential cutting the infection risks people face on daily basis. BCNet an artificial intelligence (AI)-based deep learning (DL) framework was proposed based capability transfer with convolutional neural network rapidly automatically identify eight-class scenario: Basophil, Eosinophil, Erythroblast, Immature...
A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate face mask protocol in public places. To achieve this goal, private dataset was created, including different images with and without masks. The trained to detect masks from real-time surveillance videos. detection (FMDNet) achieved promising of 99.0% terms accuracy violations (no mask) presented better capability compared other recent DL models such as...
Arabic text classification is one application of Natural Language Processing (NLP). It has been used to analyze and categorize text. Analyzing become an essential part our lives because the increasing number data which makes a big problem. systems significant maintain vital information in many domains such as education, health sector, public services. In presented research work, model developed using various algorithms namely Multinomial Naïve Bayesian (MNB), Bernoulli (BNB), Stochastic...
With the increasing number of online social posts, review comments, and digital documentations, Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within coronavirus pandemics. The variations in meaning same words could directly affect performance any AI-based framework. This work aims to identify effectiveness machine learning (ML) algorithms through preprocessing representation techniques. is...
Pandemic Patient Health Monitoring Platform (PPHMP) with the help of internet things (IoT) and cloud computing is proposed in this paper. As a result pandemic such as coronavirus outbreak, healthcare task needs system includes continuous diagnosis for monitoring patients supports decision making. The should be also helpful providers. Moreover, it accurate robust based on machine learning. PPHMP would terms its efficiency remote who are not supposed to visit hospital where health could...
Sunspots are known to be the most prominent feature of solar photosphere. Solar activities play a vital role in Space weather which greatly affects Earth's environment. The appearance sunspots determines and being observed from early eighteenth century. In this work, we have implemented deep learning model automatically detects MDI HMI image datasets. Proposed uses Alexnet based convolutional networks generate promising hierarchical features proposed approach achieved excellent...
Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, works future predict activities rare. Prediction (HAP) can benefit in multiple fall detection or exercise routines, prevent injuries. This presents a novel HAP system forecasted...
Drosophila melanogaster is an important genetic model organism used extensively in medical and biological studies. About 61% of known human genes have a recognizable match with the code flies, 50% fly protein sequences mammalian analogues. Recently, several investigations been conducted to study functions specific exist central nervous system, heart, liver, kidney. The outcomes research are also as unique tool human-related diseases. This article presents novel automated system classify...
The COVID-19 pandemic has been a global health problem since December 2019. To date, the total number of confirmed cases, recoveries, and deaths exponentially increased on daily basis worldwide. In this paper, hybrid deep learning approach is proposed to directly classify disease from both chest X-ray (CXR) CT images. Two AI-based models, namely ResNet50 EfficientNetB0, are adopted trained using public datasets, consisting 7863 2613 images, respectively used deploy, train, evaluate models....
Melanoma, a kind of skin cancer that is very risky, distinguished by uncontrolled cell multiplication. Melanoma detection the utmost significance in clinical practice because atypical border structure and numerous types tissue it can involve. The identification melanoma still challenging process for color images, despite fact approaches have been proposed research has done. In this research, we present comprehensive system efficient precise classification lesions. framework includes...
Twitter is one of the social media platforms that extensively used to share public opinions. Arabic text detection system (ATDS) a challenging computational task in field Natural Language Processing (NLP) using Artificial Intelligence (AI)-based techniques. The misogyny has received lot attention recent years due racial and verbal violence against women on platforms. In this paper, an recognition approach presented for detecting from tweets. proposed evaluated Levantine Dataset Misogynistic,...
Abstract Due to widespread usage of banana as a staple food crop and susceptibility numerous illnesses. Bananas require sophisticated detection techniques support sustainable agricultural practices. are particularly susceptible various stem leaf spot diseases, resulting in significant economic losses within the cultivation sector. In this paper, new XAI framework for disease classification is introduced. Our uses state-of-the-art AI methods analyze photos plants. With great precision, it can...
Dexterous object manipulation using anthropomorphic robot hands is of great interest for natural manipulations across the areas healthcare, smart homes, and factories. Deep reinforcement learning (DRL) a particularly promising approach to solving dexterous tasks with five-fingered hands. Yet, controlling an hand via DRL in order obtain natural, human-like high dexterity remains challenging task current robotic field. Previous studies have utilized some predefined human poses control hand’s...
Arabic Text Classification (ATC) is a crucial step for various Natural Language Processing (NLP) applications. It emerged as response to the exponential growth of online content like social posts and review comments. In this study, preprocessing techniques representation models are used evaluate effectiveness ATC using Machine Learning (ML). Generally, operation depends on factors, such stemming in preprocessing, feature extraction selection, nature dataset. To enhance overall classification...