66 hasil untuk "Sandeep Kumar Mathivanan"
Menampilkan 1–20 · 1,22 detik · RSS
-
Artikel Jurnal
A hybrid convolution and attention-based framework with visual explanation for fruit disease identification
Abstract The objective of this study is to create a highly accurate and interpretable deep learning (DL) model for the multi-class classification of fruit using convolutional and transformer architectures. The classification performance can be enhanced by making sure that the used technique is…
-
Artikel Jurnal Teks lengkap
Employing deep learning and transfer learning for accurate brain tumor detection
Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed…
Juga tersedia di: DOAJ
-
Artikel Jurnal
Corn leaf disease diagnosis: enhancing accuracy with resnet152 and grad-cam for explainable AI
Abstract Objective The agricultural sector is important in the supply of food globally as well as in enhancing the economy especially in developing countries where it forms the backbone of the economy. Corn can be classified as such crops important to the world’s food system. Unfortunately, corn…
-
Artikel Jurnal
A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns
Abstract Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM…
-
Artikel Jurnal Teks lengkap
Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis
In the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input image into semantic labels for different classes…
-
Artikel Jurnal Teks lengkap
Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis
Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing…
-
Artikel Jurnal
GNN-ML-FRL: a graph-enhanced meta-adaptive federated learning framework for scalable pest identification and ernvironmental modeling
Abstract Heterogeneous agro-ecological factors, insect breeding, and climate change are serious challenges to sustainable agricultural management. The study proposes a graph-enhanced meta-adaptive federated learning framework (GNN-ML-FRL) to address the challenges in precision agriculture. The…
-
Artikel Jurnal Teks lengkap
Federated learning based futuristic biomedical big-data analysis and standardization
Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This…
-
Artikel Jurnal
An attention-augmented multimodal classification of alzheimer’s disease and parkinson’s disease vs healthy controls using MRI, EEG, and SNP data
Abstract Due to the late manifestation of structural symptoms and symptomatic overlap, neurodegenerative diseases such as Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) remain difficult to diagnose accurately. In order to categorize AD and PD in comparison to Healthy Controls (HC), this…
-
Artikel Jurnal
Detection and classification of brain tumor using hybrid deep learning models
Abstract Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional…
-
Artikel Jurnal
Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability
Abstract The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a…
-
Artikel Jurnal
Detection and classification of adult epilepsy using hybrid deep learning approach
Abstract The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinctive nature and the subsequent negative…
-
Artikel Jurnal
Progression-guided spatiotemporal memory transformers for accurate and consistent longitudinal brain tumor segmentation
Abstract For monitoring the progression of the disease and the efficacy of treatment, it is essential to segment the brain tumor. The majority of the available deep learning models in use today are based on discrete time points without considering the continuity of the process, which causes…
-
Artikel Jurnal
DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI
Abstract Objective The primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits, particularly apples, guavas, mangoes, pomegranates, and oranges, utilizing computer vision techniques. Material An open-source collection of…
-
Artikel Jurnal
Enhancing cervical cancer detection and robust classification through a fusion of deep learning models
Abstract Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears…
-
Artikel Jurnal
Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach
Abstract Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists’ expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models…
-
Artikel Jurnal
Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
Abstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the…
-
Artikel Jurnal
Secure Healthcare Access Control System (SHACS) for Anomaly Detection and Enhanced Security in Cloud-Based Healthcare Applications
The growing reliance on distributed cloud technology in mobile healthcare applications has introduced critical challenges in ensuring secure and efficient access to Electronic Health Records (EHR). Traditional methods have prolonged authentication times and access delays, compromising both the…
-
Artikel Jurnal
IPP-DMS: A scalable privacy-preserving data management system for secure and efficient handling of large-scale datasets
Abstract The rapid growth of data in various industries has led to a pressing need for innovative solutions that ensure both the security and efficiency of data management systems. As organizations increasingly rely on large-scale datasets for decision-making, ensuring the privacy and integrity of…