Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

Internet of Things Authentication Protocols: Comparative Study

Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 andsmart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still thebiggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services providedby an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures,data, and devices. Authentication, as the first line of defense against security threats, becomes the priority ofeveryone. It can either grant or deny users access to resources according to their legitimacy. As a result, studyingand researching authentication issues within IoT is extremely important. As a result, studying and researchingauthentication issues within IoT is extremely important. This article presents a comparative study of recent researchin IoT security;it provides an analysis of recent authentication protocols from2019 to 2023 that cover several areaswithin IoT (such as smart cities, healthcare, and industry). This survey sought to provide an IoT security researchsummary, the biggest susceptibilities, and attacks, the appropriate technologies, and the most used simulators. Itillustrates that the resistance of protocols against attacks, and their computational and communication cost arelinked directly to the cryptography technique used to build it. Furthermore, it discusses the gaps in recent schemesand provides some future research directions.

Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques

Cloud computing(CC)is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services.In addition,it aims to strengthen systems and make them useful.Regardless of these advantages,cloud providers suffer from many security limits.Particularly,the security of resources and services represents a real challenge for cloud technologies.For this reason,a set of solutions have been implemented to improve cloud security by monitoring resources,services,and networks,then detect attacks.Actually,intrusion detection system(IDS)is an enhanced mechanism used to control traffic within networks and detect abnormal activities.This paper presents a cloud-based intrusion detection model based on random forest(RF)and feature engineering.Specifically,the RF classifier is obtained and integrated to enhance accuracy(ACC)of the proposed detection model.The proposed model approach has been evaluated and validated on two datasets and gives 98.3%ACC and 99.99%ACC using Bot-IoT and NSL-KDD datasets,respectively.Consequently,the obtained results present good performances in terms of ACC,precision,and recall when compared to the recent related works.

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.

Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

Human Action Recognition(HAR)attempts to recognize the human action from images and videos.The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral filters.The proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral information.To create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are combined.Linear Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate data.Finally,a closest neighbor method is used to classify the dataset.Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well.The average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,respectively.The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,respectively.From the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

IoT-Based Data Logger for Weather Monitoring Using Arduino-Based Wireless Sensor Networks with Remote Graphical Application and Alerts

In recent years,the monitoring systems play significant roles in our life.So,in this paper,we propose an automatic weather monitoring system that allows having dynamic and real-time climate data of a given area.The proposed system is based on the internet of things technology and embedded system.The system also includes electronic devices,sensors,and wireless technology.The main objective of this system is sensing the climate parameters,such as temperature,humidity,and existence of some gases,based on the sensors.The captured values can then be sent to remote applications or databases.Afterwards,the stored data can be visualized in graphics and tables form.

New Enhanced Authentication Protocol for Internet of Things

Internet of Things(IoT)refers to a new extended network that enables to any object to be linked to the Internet in order to exchange data and to be controlled remotely.Nowadays,due to its multiple advantages,the IoT is useful in many areas like environment,water monitoring,industry,public security,medicine,and so on.For covering all spaces and operating correctly,the IoT benefits from advantages of other recent technologies,like radio frequency identification,wireless sensor networks,big data,and mobile network.However,despite of the integration of various things in one network and the exchange of data among heterogeneous sources,the security of user’s data is a central question.For this reason,the authentication of interconnected objects is received as an interested importance.In 2012,Ye et al.suggested a new authentication and key exchanging protocol for Internet of things devices.However,we have proved that their protocol cannot resist to various attacks.In this paper,we propose an enhanced authentication protocol for IoT.Furthermore,we present the comparative results between our proposed scheme and other related ones.

Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection

The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data.Hence,automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes.The recognition of new elements is possible based on predefined classes.Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities.To overcome this problem,new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic.The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks.The new model is based on a proposed machine learning algorithm that comprises an input layer,a hidden layer,and an output layer.A reliable training algorithm is proposed to optimize the weights,and a recognition algorithm is used to validate the model.Preprocessing is applied to the collected traffic prior to the analysis step.This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection.

Intelligent Monitoring System for Biogas Detection Based on the Internet of Things: Mohammedia, Morocco City Landfill Case

Mechanization is a depollution activity,because it provides an energetic and ecological response to the problem of organic waste treatment.Through burning,biogas from mechanization reduces gas pollution from fermentation by a factor of 20.This study aims to better understand the influence of the seasons on the emitted biogas in the landfill of the city Mohammedia.The composition of the biogas that naturally emanates from the landfill has been continuously analyzed by our intelligent system,from different wells drilled in recent and old waste repositories.During the rainy season,the average production of methane,carbon dioxide,and oxygen and nitrogen are currently 56%,32%,and 1%,respectively,compared to 51%,31%,and 0.8%,respectively,for old waste.Hazards levels,potential fire,and explosion risks associated with biogas are lower than those of natural gases in most cases.For this reason a system is proposed to measure and monitor the biogas production of the landfill site remotely.Measurement results carried out at various sites of the landfill in the city of Mohammedia by the system show that the biogas contents present dangers and sanitary risks which are of another order.