A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing machine learning models using datasets spread across several data centers,including medical facilities,clinical research facilities,Internet of Things devices,and even mobile devices.The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information,reducing the risk of data loss,privacy breaches,or data exposure.The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources,such as patient records,medical imaging,wearable devices,and clinical research surveys.This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare.It evaluates the effectiveness of federated learning strategies in the field of healthcare.It offers a systematic analysis of federated learning in the healthcare domain,encompassing the evaluation metrics employed.In addition,this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

Mitigating and Monitoring Smart City Using Internet of Things

The present trends in smart world reflects the extensive use of limited resources through information and communication technology.The limited resources like space,mobility,energy,etc.,have been consumed rigorously towards creating optimized but smart instances.Thus,a new concept of IoT integrated smart city vision is yet to be proposed which includes a combination of systems like noise and air loss monitoring,web monitoring and fire detection systems,smart waste bin systems,etc.,that have not been clearly addressed in the previous researches.This paper focuses on developing an effective system for possible monitoring of losses,traffic management,thus innovating smart city at large with digitalized and integrated systems and software for fast and effective implementations.In our proposed system,a real time data analysis is performed.These data are collected by various sensors to analyze different factors that are responsible for such losses.The proposed work is validated on a real case study.

Improving the Detection Rate of Rarely Appearing Intrusions in Network-Based Intrusion Detection Systems

In network-based intrusion detection practices,there are more regular instances than intrusion instances.Because there is always a statistical imbalance in the instances,it is difficult to train the intrusion detection system effectively.In this work,we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances.Our technique mitigates the statistical imbalance in these instances.We also carried out an experiment on the training model by increasing the instances,thereby increasing the attack instances step by step up to 13 levels.The experiments included not only known attacks,but also unknown new intrusions.The results are compared with the existing studies from the literature,and show an improvement in accuracy,sensitivity,and specificity over previous studies.The detection rates for the remote-to-user(R2L)and user-to-root(U2L)categories are improved significantly by adding fewer instances.The detection of many intrusions is increased from a very low to a very high detection rate.The detection of newer attacks that had not been used in training improved from 9%to 12%.This study has practical applications in network administration to protect from known and unknown attacks.If network administrators are running out of instances for some attacks,they can increase the number of instances with rarely appearing instances,thereby improving the detection of both known and unknown new attacks.

Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices

The Internet of Things(IoT)is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems.These networks of wireless sensors monitor the physical environment and report the collected data to the base station,allowing for smarter decisions.Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane.However,in some application areas,such as various surveillances,underwater monitoring systems,and various environmental monitoring applications,wireless sensors are deployed in a three-dimensional plane.Recently,localization-based applications have emerged as one of the most promising services related to IoT.In this paper,we propose a novel distributed range-free algorithm for node localization in wireless sensor networks.The proposed three-dimensional hop localization algorithm is based on the distance error correction factor.In this algorithm,the error decreases with the localization process.The distance correction factor is used at various stages of the localization process,which ultimately mitigates the error.We simulated the proposed algorithm using MATLAB and verified the accuracy of the algorithm.The simulation results are compared with some of the well-known existing algorithms in the literature.The results show that the proposed three-dimensional error-correctionbased algorithm performs better than existing algorithms.

An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity.This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and grading.The proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and classification.The proposed model initially involves preprocessing stage which removes the presence of noise in the input image.Then,the watershed algorithm is applied to segment the preprocessed images.Followed by,feature extraction takes place by leveraging EOPSO-CNN model.Finally,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR images.The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way.The simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively.

Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated assistance.However,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation processes.The regular active load and inefficiency of best measures among varied associates is a huge hazard.Any sudden load deviation will give rise to immediate amendment in frequency and tie-line power errors.It is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the load.Therefore,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power grid.Balancing the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks Optimizer.We proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test bench.The experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s only.This model can also be leveraged to examine the decision boundaries of the Bilateral case.