LSGDM Two Stage Consensus Reaching Process for Autocratic Decision Making using Group Recommendations

The decision making is a general and significant action in day-to-day life. In some cases, experts cannot express their preferences using precise value due to inherent unreliability. The utilization of linguistic labels creates expert judgement more informative and consistent for decision making. The group recommendation is considered as a significant factors of e-commerce domain due to their direct impact on profit. The personalized experiments improve the engagement and the count of purchases of the customer when the recommended products are matched to the current interest.In this paper, the Large-Scale Group Decision Making (LSGDM) two stage consensus reaching process is proposed by using three various Amazon real world dataset.This proposed method permits an autocratic decision maker to utilize a different group recommendation for a sequence of decisions at highest level of consensus. The performance of the model is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall. The obtained result shows that proposed methodology provides better result while comparing various other methods

Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction

It's important to note that Alzheimer's disease can also affect individuals over the age of 60, and in fact, the risk of developing Alzheimer's increases with age. Additionally, while deep learning approaches have shown promising results in detecting Alzheimer's disease, they are not the only techniques available for diagnosis and treatment. That being said, using Region-based Convolutional Neural Network (RCNN) for efficient feature extraction and classification can be a valuable tool in detecting Alzheimer's disease. This new approach to identifying Alzheimer's disease could lead to a more accurate and personalized diagnosis. It can also help in early treatment and intervention. However, it's still important to continue developing new methods and techniques for this disorder. Considering this our work proposes an innovative Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction approach in this paper. The exhaustive experimental result carried out, which proves the efficacy of our Alzheimer prediction system

Brain tumor image identification and classification on the internet of medical things using deep learning

The health services research network is showing a lot of interest in the Internet of Medical Things (IoMT). In IoMT, the Internet is used to help compile important health-related data. A brain tumor is caused by a mass of random cells inside the brain, which is dangerous and harmful to the brain. Today, it is difficult to accurately recognise brain images. In order to find and correctly categorize malignant cells in recognizing brain pictures, this research offers a support value-based deep neural network (SDNN) in e-Health care administration utilizing the IoMT innovation. As a starting point, a database of investigation is created using picture data based on IoT innovation and clinical images. The input brain picture is subjected to skull stripping during the preprocessing stage in order to isolate the desired brain area. The preprocessed output pictures are then used to extract the useful characteristics, such as entropy, geometric, and texture features. Finally, based on the collected characteristics, the proposed support value based adaptive deep neural network (SDNN) identification classifies the brain pictures as normal or abnormal. The results of the experiments are examined to show how the suggested recognition approach outperforms the ones already in use

A bi-directional Long Short-Term Memory-based Diabetic Retinopathy detection model using retinal fundus images

Images of the retina are widely used for diagnosing fundus disease. Low-quality retinal photos make it hard for computer-aided diagnosis systems and ophthalmologists to make a clinical diagnosis. In ophthalmology, precision medicine is based partly on the quality of retinal images. Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes iris damage. It is difficult to detect and, if not detected early, can result in blindness. Convolutional neural networks are gaining popularity as an effective deep learning (DL) approach for medical image analysis. This study suggests using deep learning approaches at various stages of the fundus image-based diagnostic pipeline for diabetic retinopathy (DR). Many fields, including medical image classification, have adopted DL representations. Using retinal fundus images, we propose a bi-directional extended short-term memory-based diabetic retinopathy detection model. By examining images of the retinal fundus, the Bi-directional Long Short-Term Memory (LSTM) method can detect and classify various grades of DR. As a preprocessing step, the proposed model uses the Multiscale Retinex with Chromaticity Preservation (MSRCP) method to increase the difference of fundus pictures and progress the short difference of medicinal views. To prepare satisfactory results for image processing, multiscale retinex with chromaticity preservation is used. However, choosing the parameters’ values, such as the Gaussian scales, gain, offset, etc., is the main difficulty with the retinex algorithm. To achieve a practical effect, these parameters must be adjusted. The main goal of the suggested method is to obtain the ideal values for the parameters used in the MSRCP algorithm. Also, photos that have already been processed are used to make feature vectors with the help of an efficient net-based feature extractor that uses deep learning. Many experiments use the benchmark Methods to Evaluate Segmentation and Indexing Techniques in the Field of Retinal Ophthalmology (MESSIDOR) dataset. The results are analyzed in terms of various evaluation factors. The results show that the Bi-LSTM-MSRCP technique is better at diagnosing DR than more modern methods