Security is a crucial factor for the appropriate functioning of fog/edge computing. Secured mutual authentication in networks has become key demand as per the current security standard. Several applications are in its requirements like wireless sensor network (WSN), Distributed Systems, Micro-Cloud, Smart City, Smart Industry 4.0. Problem statement is ''Design and implementation of Fog servers and edge devices to dynamically interconnect with each other using secured mutual authentication'', which is an NP complete problem. Implementation of secured mutual authentication protocol (SMAP) using techniques of pseudo-random number generator, time-stamps and hash functions can only be considered to evaluate the best performance for connecting large number of smart devices. Our protocol avoids storing master secret keys and repetition of session keys, which makes it more secure and carries no overhead. The experimental results show that the secured mutual authentication system is efficient in comparison to recent benchmarks.INDEX TERMS Fog layer, edge layer, fog/edge security, security, mutual authentication, micro-cloud, security protocol.
The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions, results, and current status of the patient’s conditions. Successively, we will be presenting a detailed survey for the medical XAI with the model enhancements, evaluation methods, significant overview of case studies with open box architecture, medical open datasets, and future improvements. Potential differences in AI and XAI methods are provided with the recent XAI methods stated as (i) local and global methods for preprocessing, (ii) knowledge base and distillation algorithms, and (iii) interpretable machine learning. XAI characteristics details with future healthcare explainability is included prominently, whereas the pre-requisite provides insights for the brainstorming sessions before beginning a medical XAI project. Practical case study determines the recent XAI progress leading to the advance developments within the medical field. Ultimately, this survey proposes critical ideas surrounding a user-in-the-loop approach, with an emphasis on human–machine collaboration, to better produce explainable solutions. The surrounding details of the XAI feedback system for human rating-based evaluation provides intelligible insights into a constructive method to produce human enforced explanation feedback. For a long time, XAI limitations of the ratings, scores and grading are present. Therefore, a novel XAI recommendation system and XAI scoring system are designed and approached from this work. Additionally, this paper encourages the importance of implementing explainable solutions into the high impact medical field.
The wood-based furniture manufacturing industries prioritize quality of production to meet higher market demands. Identifying various types of edge-glued wooden panel defects are a challenge for a human worker or a camera. Several studies have shown that the detection of edge-glued defects with low, high, normal, overlong, short is identified but detection of residue and bluntness is highly challenging. Thus, the present model identifies defects of low, high, normal, overlong, short by computer vision and/or deep learning, whereas defects of residue and bluntness by deep learning based decide by pass for having better performance. The goal of this paper is to provide an improved defect detection solution for wood-based furniture manufacturing industries by process automation. Therefore, a system was designed that takes defect input images from a camera as raw image and laser-aligned image for defect detection of the edge-glued wooden panel. The process automation then performs computer vision-based image features extraction with deep learning for defect detection. The aim of this paper is to solve edge-glued defect detection problems by using design and implementation of edge-glued wooden defect detection, that can be stated as edge-glued wooden panel defect detection using deep learning (WDD-DL) for process automation by artificial intelligence and Automated Optical Inspection (AOI) consolidation. Possibly there exist several types of defects on the edges while edge-banding on the wooden panel in furniture manufacturing. Therefore, the scope is to achieve higher accuracy by raw image and laser-aligned image feature extraction using deep learning algorithms for final result defect classification in WDD-DL by AOI. The WDD-DL system uses Gabor, Harris corner, morphology, structured light detection and curvature calculation for pre-processing and InceptionResnetV2 Convolutional Neural Network algorithm to attain the best results. The applications of this work can be found in quality control of the furniture manufacturing industry for an edge, corner, joint defect detection of the wooden panels. The WDD-DL achieves best results as the precision, recall and F1 score are 0.97, 0.90 and 0.92, respectively. The experiments demonstrate higher accuracy achievement as compared to other methods with overkill and escape rate analysis. Ultimately, the discussion section provides an interesting experience sharing about the necessary factors for implementing the WDD-DL in real-time industrial operations.
Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clinical parameters, but they lack predicting the recovery status after starting treatment. The goal of this paper is to provide a pneumonia status prediction system for the early affected patient’s discharge from the hospital within 7 days or late discharge more than 7 days. This paper aims to design a multimodal data analysis for pneumonia status prediction using deep learning classification (MDA-PSP). We have developed a system that takes an input of vital signs and CXR images of the affected patient with pneumonia from admission day 1 to day 3. The deep learning then classifies the health status improvement or deterioration for predicting the possible discharge state. Therefore, the scope is to provide a highly accurate prediction of the pneumonia recovery on the 7th day after 3-day treatment by the SHAP (SHapley Additive exPlanation), imputation, adaptive imputation-based preprocessing of the vital signs, and CXR image feature extraction using deep learning based on dense layers-batch normalization (BN) with class weights for the first 7 days’ general ward patient in MDA-PSP. A total of 3972 patients with pneumonia were enrolled by de-identification with an adult age of 71 mean ± 17 sd and 64% of them were male. After analyzing the data behavior, appropriate improvement measures are taken by data preprocessing and feature vectorization algorithm. The deep learning method of Dense-BN with SHAP features has an accuracy of 0.77 for vital signs, 0.92 for CXR, and 0.75 for the combined model with class weights. The MDA-PSP hybrid method-based experiments are proven to demonstrate higher prediction accuracy of 0.75 for pneumonia patient status. Henceforth, the hybrid methods of machine and deep learning for pneumonia patient discharge are concluded to be a better approach.
Early prediction of clinical deterioration such as adverse events (AEs), improves patient safety. National Early Warning Score (NEWS) is widely used to predict AEs based on the aggregation of 6 physiological parameters. We took the same parameters as the features for AE prediction using deep learning algorithms (AEP-DLA) among hospitalized adult patients. The aim of this study is to get better performance than traditional naïve mathematical calculations by introducing novel vital sign data preprocessing schemes. We retrospectively collected the data from our electronic medical record data warehouse (2007 ~ 2017). AE rate of all 99,861 admissions was 6.2%. The dataset was divided into training and testing datasets from 2007-2015 and 2016-2017 respectively. In real-life clinical care, physiological parameters were not recorded every hour and missed frequently, for example, Glasgow Coma Scale (GCS). The expert domain suggested that missed GCS was rated as 15. We took two strategies (stack series records and align by hour) in the data preprocessing and tripling the values of negative samples for class balancing (CB). We used the last 28 hours' serial data to predict AEs 3 hours later with Random Forest, XGBoost, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). It is shown that CNN with CB and align by hour got the best results comparing to the other methods. The precision, recall and area under curve were 0.841, 0.928 and 0.995 respectively. The performance of the model is also better than those proposed in the published literatures.
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