Author details
Author's Published Works
Author details
Showing 1-21 of 21 results
- Sort
Filter Results
Show
Sort By
Results
Decoding Disease Intensity: Predicting Pythium Severity with CNN-SVM Hybrid Models
2024 5th IEEE Global Conference for Advancement in Technology (GCAT)
Year: 2024 | Conference Paper |
A precise and prompt evaluation is required to establish efficient disease control measures because Pythium disease poses a serious hazard to agricultural products. The severity levels of the Pythium disease in crops are divided into five classes in this study report, with 1-20%, 20-40%, 40-60%, 60-80%, and 80-100% being the most severe. To extract useful information from high-resolution photos of...Show More
Shielding Lemongrass: An Integrated Multimodal Strategy for Disease Detection with CNNs and Random Forests
2024 5th IEEE Global Conference for Advancement in Technology (GCAT)
Year: 2024 | Conference Paper |
Diseases that affect lemongrass can have a major influence on crop output, thus prompt and correct diagnosis is required. In the article, the Scientist introduces a novel method using Convolutional Neural Networks and Random Forest algorithms to forecast six prevalent illnesses in lemongrass, which is including Yellow Mosaic Disease, Rust, Leaf Blight, Sooty Mold, Red Root, & Damping-Off. A thorou...Show More
Robust Drowsiness Detection Using Convolutional Neural Networks: A Balanced Approach to Driver Fatigue Monitoring
2024 International Conference on Big Data Analytics in Bioinformatics (DABCon)
Year: 2024 | Conference Paper |
Drowsiness detection is a very critical concern to guarantee safety, above all in the case of driving, since it may cause serious accidents. Traditional approaches to drowsiness detection are based on physiological signals or visual cues, but they usually fail in giving accurate results or are irritatingly intrusive. In this work, we investigate the adoption of a CNN model in classifying states of...Show More
Innovative Fare Collection Systems: Integrating IoT for Enhanced Urban Mobility and Sustainability
2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT)
Year: 2024 | Conference Paper |
This research study examines the effects of IOT-enabled smart fare-collecting systems on urban transportation, emphasizing its operational, economic, and environmental advantages. The research assesses case implementations in Singapore, London, and New York City, using statistical techniques to measure the efficacy of these systems. The investigation indicates that IoT -based fare collecting may d...Show More
A problem that is escalating on a global level and demands fresh approaches to improve the use of water resources is water scarcity. This research examines the potential of IoT-based Smart Water Management Systems (SWMS) in addressing these challenges through real-time data for water supply, leak detection and quality checks. Five case studies showed that IoT systems reduced total water use by 25 ...Show More
High-Precision Fracture Identification: The Role of Convolutional Neural Networks in Orthopedics
2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)
Year: 2024 | Conference Paper |
The research work is related to diagnosing a bone fracture using CNN on X-ray images. The collection is done by combining multi-region X-ray images and classifying them into two classes: bone fractured and non-fractured. This work illustrates a more precise classifying automated fracture detection, which is one of the critical needs in diagnoses of medicine. For effective treatment, early and prec...Show More
Automated Classification of Kidney Diseases Using Deep Learning: A Comparative Analysis with Machine Learning Models on CT Images
2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)
Year: 2024 | Conference Paper |
Kidney disease diagnosis with the use of a medical image plays an important role in diagnosis and treatment on time. This survey presents a comparison between different machine learning algorithms and deep learning models that classify kidney conditions from the CT KIDNEY DATASET, containing four distinct classes: Normal, Cyst, Tumor, and Stone. The authors have reviewed the performance of CNNs wh...Show More
Securing Data Privacy in Machine Learning: The FedAvg of Federated Learning Approach
2024 4th Asian Conference on Innovation in Technology (ASIANCON)
Year: 2024 | Conference Paper |
This research evaluates the FedAvg method that is created for picture categorization, particularly the important aspects of privacy and data security in the machine learning field. The research employs a dataset that has 60.000 training photos and 10.000 test images with ten labels that correspond to different fashion items classifying. The FedAvg technique trains a CNN model by negotiating model ...Show More
Hybrid AI Models for Dermatology: Combining CNN and RF for Skin Lesion Classification
2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI)
Year: 2024 | Conference Paper |
Accurate and timely diagnosis of skin lesions is crucial for effective treatment. This research proposes a novel hybrid model that combines Convolutional Neural Networks (CNNs) and Random Forests (RFs) to improve the classification of skin lesions. The CNN component extracts relevant features from dermoscopic images, while the RF classifier categorizes these features into different skin lesion typ...Show More
WakeGuard: Enhancing Safety with Deep Learning-Based Drowsiness Detection Using VGG16
2024 International Conference on Cybernation and Computation (CYBERCOM)
Year: 2024 | Conference Paper |
Drowsiness detection is paramount to avoiding accidents and ensuring safety in transport and industrial operations. This paper presents a robust system for drowsiness detection by utilizing transfer learning from a pre-trained VGG16 model. Classification will be done based on distinguishing the subject's state: closed eyes, eyes open, yawning, and no yawning. This project has an appropriate divisi...Show More
Advancing Table Tennis Analytics: A CNN-LSTM Framework for Precision Serve Shot Analysis
2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)
Year: 2024 | Conference Paper |
As for the current work, this research proposes a CNN-LSTM model systematized for evaluating and predicting the serve strokes in real-time table tennis. It is supposed to address the deficiency of extensive and timely sports analysis by combining the spatial FE capability of Convolutional Neural Networks (CNN) with the temporal dynamics learning function of Long Short-Term Memory (LSTM) networks. ...Show More
Dynamic Treatment Strategies: The RL-CNN Model in Personalized Medicine
2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)
Year: 2024 | Conference Paper |
Personalized medicine offers the potential to revolutionize healthcare by customizing treatments to individual patient characteristics. This study proposes a novel Reinforcement Learning-Convolutional Neural Network (RLCNN) hybrid model to optimize personalized treatment schedules, specifically focusing on medication dosage and timing. The RL-CNN model was trained on a comprehensive dataset of pat...Show More
The Future of Orthopedic Care: High-Accuracy Bone Fracture Detection with CNNs
2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)
Year: 2024 | Conference Paper |
Bone fractures are a severe health issue and affect millions of people across the globe. Their diagnosis must thus be timely and correct for appropriate treatment. Conventional diagnosis of bone fracture is mainly done through manual examination of radiographic evidence on X-rays, and hence, it suffers from variability. To address this challenge, this paper proposes a deep learning-based approach ...Show More
Privacy-Preserving AI: Leveraging Federated Reinforcement Learning in Distributed Systems
2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)
Year: 2024 | Conference Paper |
Organizing computing distributed resources in conjunction with machine learning algorithms in the age of big data is critical. This paper presents the FRL approach that combines Federated Learning and Reinforcement Learning to address problems in scale, speed, and data privacy. The overall framework of the Federated Reinforcement Learning (FRL) model bestows the use of distributed computing at dif...Show More
Real-Time Monitoring and Control of Drones Using IoT-Blockchain Integration
2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)
Year: 2024 | Conference Paper |
This work presents an IoT-Blockchain integration solution to enhance the typical Drone systems to acquire optimal organizational performance. Several questions that are associated with modern drone operations are solved by the suggested system, and these questions are the following: data security, efficiency of work, and system scalability. Concerning the experimental results, the boost in the rea...Show More
Expanding the Horizons of Chinese Text Classification with Federated Learning Across Domains
2024 4th International Conference on Intelligent Technologies (CONIT)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Federated learning (FL) is a new technology that can correct privacy issues in machine learning by training models on dispersed units. Through applying a hybrid algorithm comprising Federated Learning (FedLearning), Local Batch Normalization (LBN), and Parameter Factorization Weighting (PFW), the article now introduces a Chinese text classifier called the FedBN-PW-CTC Model. This model intends to ...Show More
Artificial Intelligence-Based Customer Behavioral Analysis: Techniques, Applications, and Business Insights
2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)
Year: 2023 | Conference Paper |
Artificial Intelligence (AI) has revolutionized the way businesses get their client behavior, empowering a more profound understanding of shopper inclinations and impelling the advancement of focused showcasing techniques. This paper centers on the application of the K-means clustering calculation inside AI for client behavioral investigation. The point is to investigate the procedures, applicatio...Show More
Attack Detection in Wireless Sensor Networks using Novel Artificial Intelligence Algorithm
2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The Wireless Sensor Network (WSN) is a concept of an Internet of Things network (IoT) with limited energy resources. When many sensors are used despite their limitations, security becomes a significant concern in the network, necessitating the practical design of detection and mitigation techniques. Using Artificial Intelligence (AI) algorithms is a very effective way to develop cyber-attack detec...Show More
An Exploration Of An Operational Multi-Data-Set Kernel Culture Scheme For Practice In Data Mining
2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP)
Year: 2023 | Conference Paper |
Data-centric solutions are vital to all businesses, media, and research. Since data size matters, knowledge mining from massive data requires automation. The world awaits machine learning with AI. Industrial productions to purchase decisions rely on knowledge-mined solutions. Machine learning benefits medical research. This study examines the elements that influence the effectiveness of machine le...Show More
Improving Efficient Daytime Luminance and Improvement of Daylight Feasibility Using Google Net Convolutional Neural Network
2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)
Year: 2023 | Conference Paper |
Day lighting is one of the effective approaches to monitor natural light penetration through the building’s interior surfaces. In energy efficient buildings, lighting regulation combined with day lighting is an effective and useful technique. Daytime estimation is a crucial achievement in daytime designs. The measurement of daylight illuminances can be achieved through the lighting simulation sche...Show More
Blockchain Technologies in 5g and Beyond Connections: A Review of the Taxonomy, Practice Area, Prospects, and Challenges
2023 International Conference on Artificial Intelligence and Smart Communication (AISC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Every development in communication standards up to this point has been driven by the need to provide end users with high-speed access. Although future-proof 5G and well beyond systems are being designed to accommodate the varying needs of a wide range of use cases, the emphasis has shifted significantly due to 5G. These requirements encompass Accelerated Wireless Internet, Massive Computer Telecom...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.