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  • 1. Tumor Localization and Classification from MRI of Brain using Deep Convolution Neural Network and Salp Swarm Algorithm

    La description:
    Early diagnosis of brain tumors is crucial for treatment planning and increasing the survival rates of infected patients. In fact, brain tumors exist in a range of different forms, sizes, and features, as well as treatment choices. One of the essential roles of neurologists and radiologists is the diagnosis of brain tumors in their early stages. However, manual brain tumor diagnosis is difficult, time-consuming, and prone to error. Based on the problem highlighted, an automated brain tumor detection system is mandatory to identify the tumor in its initial stages. This research presents an efficient deep learningbased system for the classification of brain tumors from brain MRI using the deep convolutional network and salp swarm algorithm. All experiments are performed using the publicly available brain tumor Kaggle dataset. To enhance the classification rate, preprocessing and data augmentation such as skewed data ideas are devised. In addition, AlexNet and VGG19 are leveraged to perform specific functionality. Finally, all features merged into a single feature vector for brain tumor classification. Some of the extracted features found insignificant towards effective classification. Hence, we employed an efficient feature selection technique named slap swarm to find the most discriminative features to attain best tumor classification rate. Finally, several SVM kernels are merged for the final classification and 99.1% accuracy is achieved by selecting 4111 optimal features from 8192.
    Mot-clé:
    MRI, Health risks, Public health, Brain tumor, Deep learning, and Transfer learning
    Assujettir:
    Artificial Intelligence and Data Science
    Créateur:
    Fayyaz, Abdul Muiz , Rehman, Amjad , Alyami, Jaber , Alkhurim, Alhassan , Almutairi, Fahad , Saba, Tanzila , and Roy, Sudipta
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Éditeur:
    Springer Nature
    Emplacement:
    Switzerland
    La langue:
    English
    Date de téléchargement:
    11-02-2023
    Date modifiée:
    16-02-2023
    date créée:
    01-01-2023
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identificateur:
    10.1007/s12559-022-10096-2

  • 2. Multi-Controller Model for Improving the Performance of IoT Networks

    La description:
    Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
    Mot-clé:
    Layered Networking, Parallel Architectures, Device Level Clustering, Internet of Things, Topology Binding, Performance Optimization, and Radio-frequency Identification
    Assujettir:
    Artificial Intelligence and Data Science
    Créateur:
    Singh, Ninni , Roy, Sudipta , Saad, Ismail , Rahebi, Javad , Farzamnia, Ali , Gunjan, Vinit Kumar , Davanam, Ganesh , and Kallam, Suresh
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Éditeur:
    MDPI
    Emplacement:
    Switzerland
    La langue:
    English
    Date de téléchargement:
    11-02-2023
    Date modifiée:
    16-02-2023
    date créée:
    01-11-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identificateur:
    10.3390/en15228738

  • 3. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

    La description:
    The global healthcare sector continues to grow rapidly and is reflected as one of the fastestgrowing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
    Mot-clé:
    Healthcare, Precision Medicine, Artificial Intelligence, Computer Vision, Deep Learning, Medical Imaging, XAI, and Supervised Learning
    Assujettir:
    Artificial Intelligence and Data Science
    Créateur:
    Roy, Sudipta , Lim, Se-Jung , and Meena, Tanushree
    Donateur:
    Jio Institute, CVMI-Computer Vision in Medical Imaging Project
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Éditeur:
    MDPI
    Emplacement:
    Switzerland and India
    La langue:
    English
    Date de téléchargement:
    10-02-2023
    Date modifiée:
    16-02-2023
    date créée:
    01-10-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identificateur:
    10.3390/diagnostics12102549