Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches.

This study sought to investigate how different brain regions are affected by Alzheimer's disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer's disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer's disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.

Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.

Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient's lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets-multi-ROI and single-ROI-are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.

An Optimized Approach to Channel Modeling and Impact of Deteriorating Factors on Wireless Communication in Underground Mines.

The environment of underground coal mines has challenging properties that makes this zone inadaptable for a stable communication system. Additionally, various deteriorating physical parameters strongly affect the performance of wireless networks, which leads to limited network coverage and poor quality of data communication. This study investigates the communication capability in underground coal mines by optimizing the wireless link to develop a stable network for an underground hazardous environment. A hybrid channel-modeling scheme is proposed to characterize the environment of underground mines for wireless communication by classifying the area of a mine into the main gallery and sub-galleries. The complex segments of mine are evaluated by categorizing the wireless links for the line-of-sight (LOS) zones and hybrid modeling is employed to examine the characteristics of electromagnetic signal propagation. For hybrid channel modeling, the multimode waveguide model and geometrical optic (GO) model are used for developing an optimal framework that improves the accessibility of the network in the critical time-varying environment of mines. Moreover, the influence of various deteriorating factors is analyzed using 2.4 GHz to 5 GHz frequency band to study its relationship with the vital constraints of an underground mine. The critical factors such as path loss, roughness loss, delay spread, and shadow fading are examined under detailed analysis with variation in link structure for the mine.