Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review.

The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images.

Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.

An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep.

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.

Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease.

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects.

Comparative performance assessment of deep learning based image steganography techniques.

Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganography techniques were proposed in the literature for hiding a secret image by embedding it into cover media.

KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image.

Purpose: The alarming increase in diseases of urinary system is a cause of concern for the populace and health experts. The traditional techniques used for the diagnosis of these diseases are inconvenient for patients, require high cost, and additional waiting time for generating the reports. The objective of this research is to utilize the proven potential of Artificial Intelligence for organ segmentation.

Spatial feature and resolution maximization GAN for bone suppression in chest radiographs.

Background And Objective: Chest radiographs (CXR) are in great demand for visualizing the pathology of the lungs. However, the appearance of bones in the lung region hinders the localization of any lesion or nodule present in the CXR. Thus, bone suppression becomes an important task for the effective screening of lung diseases.

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet.

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance.

Applying deep learning-based multi-modal for detection of coronavirus.

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus.

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.

In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world.

A deep learning model for mass screening of COVID-19.

The objective of this research is to develop a convolutional neural network model 'COVID-Screen-Net' for multi-class classification of chest X-ray images into three classes viz. COVID-19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X-ray images and accurately identifies the features responsible for distinguishing the X-ray images of different classes.