Location Aware Social Networks User Profiling Using Big Data Analytics

You In most cases, doctors and the use of image processing tools can assess the placenta—the actual site of physical birth—during pregnancy. Models built using Machine Learning produce performance metrics such as Accuracy, ROC, Precision, Recall, and F-Measure, which quantify this support. This research makes use of the tailored strategy built into the Weka tool, namely the DeepLearning4j package.Using common architectures such as LeNet, VGGnet, ResNet, and Alexnet yields results that are comparable. By iterating over the input layer's Loss functions and the output layer's errors, DeepLearning4j evaluates and optimizes the suggested architecture based on its quality. The experimental results show that out of seven distinct loss functions, the one with the abbreviation "MCXENT" (meaning "Multi-class Cross Entropy") produces the most accurate and least error-prone results. Additionally, a maximum accuracy of 95.7% is obtained, which is considered the best performance. These findings lend credence to an additional machine learning strategy that employs an interactive development tool for gynecologists and offer fresh assurance in its efficacy.

Section: Using Ultrasonography To Diagnose Placental Problemsmentioning

confidence: 99%

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Madhu,

Nethra Betgeri,

Pavan

et al. 2024

Progressive Collaborative Method for Protecting Users Privacy in Location-Based Services

Ramakrishna Reddy,

Sharma,

Anusha

et al. 2024

The development of new mobile communication and information service technologies has opened up exciting possibilities for location-based services. Users of location-based services (LBS) can access vital data from their service providers by utilizing their location data. Maps and navigation, information services, tourist information services, social networking, and many more popular applications are available. A user's location and other personal details must be submitted to the providers of location-based services in order for them to work. For example, details about one's whereabouts and identity. By "location privacy," we mean the idea that third parties shouldn't be able to track a user's precise whereabouts. It is important that users' sensitive information be hidden from unauthorized individuals when communicating. Most difficult in LBS location-based are concerns about communications and data. Each peer does their duty reciprocally in a collaborative method, which is a completely distributed technique. For the most secure and private location-based services (LBS), it employs cryptographic methods. The number of people using LBS is growing at a rapid pace these days. At this time, there isn't a single method available that has scalability capabilities. Building a realistic and computationally efficient solution that offers high privacy while decreasing processing overhead and improving scalability is a challenging task. The suggested method is cost-effective, supports scaling, is highly resilient against security and privacy assaults, and ensures privacy.

Image reconstruction techniques using deep learning quality segmentation

Rajya Lakshmi,

Suresh,

Mutalik Desai

et al. 2024

Translational CT (TCT), in developing nations, a low-end computed tomography (CT) technology are relatively common. The limited-angle TCT scanning mode is often used with large-angle scanning to scan items within a narrow angular range, reduce X-ray radiation, scan long objects, and prevent detector discrepancies.. However, this scanning mode greatly reduces the picture quality and diagnostic accuracy due to the added noise and limited-angle distortions. A U-net convolutional neural network-based approach for limited-angle TCT image reconstruction has been created to reconstruct a high-quality image for the limited-angle TCT scanning mode (CNN). The limited-angle TCT projection data are first examined using the SART method, and the resulting picture is then fed into a trained CNN that can reduce artifacts and maintain structures to provide a better reconstructed image. Simulated studies are used to demonstrate the effectiveness of the algorithm designed for the limitedangle TCT scanning mode. In contrast to certain modern techniques, the developed algorithm considerably lowers noise and limited-angle artifacts while maintaining image structures.