Multi-Lingual Character Recognition and Extraction using Recurrent Neural Networks
A Novel Approach to Smart Autonomous Monitoring of Indoor Plant Health Based on Leaf Color
Edge Preserving Image Denoising with Deep Networks and Ensemble Convolutional Neural Network Architectures
Neural Network Approach to Detect Renal Calculi
Enhancing Fabric Quality Control: Implementing Real-Time Defect Detection with Image Processing Techniques and Arduino
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
Automated defect detection in medical imaging has become an emerging field in several medical diagnostic applications. Automated detection of tumors in MRI is crucial as it provides information about abnormal tissues that are necessary for treatment. The conventional method for defect detection in magnetic resonance brain images is human inspection. This method is impractical due to the large amount of data. Hence, trusted and automatic classification schemes are essential to preventing the human death rate. So, automated tumor detection methods are being developed to save radiologist time and obtain tested accuracy. MRI brain tumor detection is a complicated task due to the complexity and variability of tumors. In this work, machine learning algorithms are proposed to overcome the drawbacks of traditional classifiers when tumors are detected in brain MRIs using machine learning algorithms. The outcome of the model is to predict whether a tumor is present or not in the image.
An image caption generator is essential for social media enthusiasts or visually impaired individuals. It can be used as a plugin in popular social media platforms to recommend suitable captions or to assist visually impaired people in comprehending the image content on the web, thereby eliminating ambiguity in image meaning and ensuring accurate knowledge acquisition. This research describes an image caption generator that utilizes a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model to generate natural language descriptions of images. The CNN was employed to extract features from the input image, which were then fed into the LSTM to generate the corresponding caption. The model is trained on a large dataset of image-caption pairs, using a combination of supervised and reinforcement learning techniques. The model's performance is evaluated using several metrics, and the results demonstrate that the proposed CNN LSTM model outperforms existing state-of-the-art approaches in generating accurate and diverse image captions. This model has the potential to be used in various applications, including image retrieval, content-based image search, and assisting visually impaired individuals to understanding their surroundings. It also discusses about the structure and functions of the various neural networks involved.
This research focused on addressing the common occurrence of depression in individuals with Parkinson's Disease (PD), a neurodegenerative disorder. Depression can significantly affect a person's functioning, making early detection crucial for effective treatment. The analysis explored the use of voice recordings from PD patients to extract paralinguistic features, which are non-verbal elements of speech such as tone, pitch, and rhythm. These features were then utilized to train Machine Learning and Deep Learning models with the objective of predicting depression. The results of the research revealed promising outcomes, with the models achieving accuracies as high as 0.77 in accurately classifying subjects as depressed or non-depressed. These findings suggest that voice recordings can serve as digital biomarkers to screen for depression among PD patients. By leveraging these paralinguistic features, healthcare professionals could potentially identify depression in PD patients at an earlier stage, facilitating prompt intervention and enhancing treatment outcomes. The implications of this research are as follows. Implementing voice-based screening tools could offer a non-invasive and easily accessible method to assess the mental well-being of PD patients. Such early detection could help clinicians to tailor treatment plans accordingly, ensuring that patients receive appropriate care for both PD and comorbid depression. Ultimately, the integration of voice-based screening into routine clinical practice has the potential to improve the overall quality of patients with PD, leading to better mental health outcomes.
This paper provides an overview of the phases, methods, and datasets used in modern Facial Emotion Recognition (FER). FER has been a crucial topic in computer vision and Machine Learning (ML) for decades. By using Convolutional Neural Networks (CNN) to recognize facial expressions, valuable insights into people's emotional states can be gained, leading to improved services such as personalized healthcare, enhanced customer service, and more effective marketing. Automated FER can be used in various settings, including healthcare, education, criminal investigations, and Human Robot Interface (HRI). The study includes a comparative analysis of the performance and conclusions of several models such as Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50), MobileNet, Deep CNN and the proposed pretrained VGG 16 architecture. These models can be integrated into different systems for various purposes such as obtaining feedback on products, services, or virtual learning platforms. Ultimately, Facial Emotion Recognition using Convolutional Neural Networks (CNN) can help reduce bias in decision-making processes by providing an unbiased assessment of a person's emotional state.
This research paper explores the application of satellite image processing techniques for crop detection in the agricultural industry. The primary objective is to provide insights into how these techniques can enhance crop yield and reduce losses, thereby contributing to global food security. The analysis employs advanced technologies and analytical methods to process satellite images and extract valuable information pertaining to crop growth and health. The findings demonstrate that satellite image processing offers accurate and timely data on crop conditions, enabling farmers to make well-informed decisions regarding irrigation, fertilization, and pest control. By harnessing these technologies, a more sustainable and productive agricultural system can be established to address the challenge of feeding a growing population. This research contributes to the expanding body of knowledge surrounding the use of satellite image processing for crop detection and establishes a foundation for further exploration and development of these technologies in agriculture. The analysis highlights the potential benefits of employing satellite image processing techniques for crop monitoring and management. The ability to analyze large-scale agricultural landscapes using satellite imagery provides a comprehensive and cost-effective approach for monitoring crop growth and detecting anomalies or areas of concern. It emphasizes the significance of accurate and timely data for optimizing resource allocation and improving crop management practices. By leveraging the insights gained from satellite image processing, farmers can adopt proactive measures to optimize their farming operations, enhance productivity, and mitigate potential risks. This research contributes valuable knowledge to the utilization of satellite image processing for crop detection, offering promising possibilities for the advancement of agriculture and addressing global food security challenges.
