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
Digital images play a very important role in developing computer-aided systems. The motion blur and blur in such types of images affect the accuracy of the system. Therefore, it is a challenging task to estimate and remove the blur in the images. In the present paper, an attempt is made to use a Convolutional Neural Network (CNN) model to estimate and remove the blur in the images. The CNN model with different functions helps to improve the accuracy of removing blur from the images. Different network functions, such as ReLU and Sigmoid, and their combinations are analyzed for the modeling of CNN. The performance of CNN is analyzed with different parameters, such as blur estimation, PSNR, RMSE, SSIM, and MSE. The performance is measured by considering different image categories, such as more blur images, less blur images, dark blur images, and biomedical images. Considering the parameters, it is observed that CNN with ReLU and Sigmoid functions is giving better performance than other network functions. It is observed that CNN models are giving successful performance to remove blur and correct the blur than any other traditional models.
There has been growing interest in using neuroimaging data, such as MRI scans, for the detection of Alzheimer's Disease (AD). Computer vision and deep learning models have shown promise in developing effective Computer-Aided Diagnosis (CAD) models for AD detection and classification. However, many existing models struggle due to their reliance on large training datasets and effective hyper parameter tuning strategies. To address these issues, transfer learning is often used to adjust the final fully connected layers of pre-trained DL models for use with smaller datasets. This paper proposes a new AD classification model based on a combination of Wasserstein GAN-Gradient Penalty (WGANGP) and Deep Transfer Learning (DTL) techniques, aimed at achieving accurate identification and classification of AD on 3D MRI scans. The WGANGP technique is used to increase the size of the dataset, and the model utilizes image enhancement and 3D Spatial Fuzzy C-means (3DS-FCM) techniques for image segmentation. Additionally, feature extraction is performed using the Ant Lion Optimizer (ALO) with the Inception v3 model, while the Deep Belief Network (DBN) model is employed for AD classification. The experimental validation of the WGANGP-DTL model is conducted using a benchmark 3D MRI dataset, and the results show that the proposed model outperforms recent approaches in several aspects.
Internet-based applications are making the habitual society and exploring new ways to perform online-based crimes. Numerous cybercriminals are engaged in the different platforms of the internet-based virtual world, carrying out cybercrimes according to predetermined and preplanned agendas. As technology advances, cyberstalking, cyberbullying, and other forms of cyber harassment are growing on social media, email, and other online platforms. Cyberstalking uses internet-based technology to harass, intimidate, and undermine individuals online with different approaches. In order to examine the impact of feature selection strategies for improving model performance, this paper proposes a machine learning-based cyberstalking detection model. The proposed model used the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to extract features, and three distinct approaches, TF-IDF + Chi-Square Test, and TF-IDF + Information Gain, were used to select the different numbers of relevant features. In the cyberstalking detection model, a Support Vector Machine (SVM) was employed for classification purposes. Based on the SVM classifier's performance, each feature selection approach's impact on the various feature sets was assessed. According to experimental findings, the TF-IDF + Chi-Square Test outperformed other applied approaches and improved detection mode performance. Additionally, experimental findings demonstrate that the TFIDF + Chi-Square Test approach also performs better in a small collection of relevant features than other approaches that have been utilized.
Remote Sensing Images (RSI) are captured by the satellites. The quality of the RSIs primarily depends on environmental conditions and image-capturing device capability. Rapid development in technology leads to the generation of High- Resolution (HR) images from satellites. However, these images are to be processed in a scientific way for the best results. A new Image Fusion (IF) technique with the help of wavelets, Deep Convolutional Generative Adversarial Networks (DCGAN), was designed to get super-resolution images for satellite images. Residual Convolution Neural Network (ResNet) increases the fused image accuracy by minimizing the vanishing gradient problem. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Feature Similarity Index Method (FSIM), and Universal Image Quality (UIQ) are taken as the metrics for comparing the results with other models. The experimental results are better than previous methods and minimize the spatial and spectral losses during the fusion.
The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle.
