Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its users.But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders.Intrusion Detection System(IDS)refers to one of the commonly utilized system for detecting attacks on cloud.IDS proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere identified.The current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature engineering.This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security(ECODL-IDSCS)model.The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)technique.For detecting and classification of intrusions,long short term memory(LSTM)model is exploited.In addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment.Once the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s knowledge.In this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class labels.In order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning

In recent years,the demand for biometric-based human recog-nition methods has drastically increased to meet the privacy and security requirements.Palm prints,palm veins,finger veins,fingerprints,hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition techniques.Amongst the available biometric recognition techniques,Finger Vein Recognition(FVR)is a general technique that analyzes the patterns of finger veins to authenticate the individuals.Deep Learning(DL)-based techniques have gained immense attention in the recent years,since it accomplishes excellent outcomes in various challenging domains such as computer vision,speech detection and Natural Language Processing(NLP).This technique is a natural fit to overcome the ever-increasing biomet-ric detection problems and cell phone authentication issues in airport security techniques.The current study presents an Automated Biometric Finger Vein Recognition using Evolutionary Algorithm with Deep Learning(ABFVR-EADL)model.The presented ABFVR-EADL model aims to accomplish bio-metric recognition using the patterns of the finger veins.Initially,the presented ABFVR-EADL model employs the histogram equalization technique to pre-process the input images.For feature extraction,the Salp Swarm Algorithm(SSA)with Densely-connected Networks(DenseNet-201)model is exploited,showing the proposed method’s novelty.Finally,the Deep-Stacked Denoising Autoencoder(DSAE)is utilized for biometric recognition.The proposed ABFVR-EADL method was experimentally validated using the benchmark databases,and the outcomes confirmed the productive performance of the proposed ABFVR-EADL model over other DL models.

Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

Cloud Computing(CC)provides data storage options as well as computing services to its users through the Internet.On the other hand,cloud users are concerned about security and privacy issues due to the increased number of cyberattacks.Data protection has become an important issue since the users’information gets exposed to third parties.Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools.Intrusion Detection Systems(IDSs)can be used in a network to manage suspicious activities.These IDSs monitor the activities of the CC environment and decide whether an activity is legitimate(normal)or malicious(intrusive)based on the established system’s confidentiality,availability and integrity of the data sources.In the current study,a Chaotic Metaheuristics with Optimal Multi-Spiking Neural Network-based Intrusion Detection(CMOMSNN-ID)model is proposed to secure the cloud environment.The presented CMOMSNNID model involves the Chaotic Artificial Bee Colony Optimization-based Feature Selection(CABC-FS)technique to reduce the curse of dimensionality.In addition,the Multi-Spiking Neural Network(MSNN)classifier is also used based on the simulation of brain functioning.It is applied to resolve pattern classification problems.In order to fine-tune the parameters relevant to theMSNN model,theWhale Optimization Algorithm(WOA)is employed to boost the classification results.To demonstrate the superiority of the proposed CMOMSNN-ID model,a useful set of simulations was performed.The simulation outcomes inferred that the proposed CMOMSNN-ID model accomplished a superior performance over other models with a maximum accuracy of 99.20%.

Automated Artificial Intelligence Empowered White Blood Cells Classification Model

White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Due to the rapid increase in urbanization and population,crowd gatherings are frequently observed in the form of concerts,political,and religious meetings.HAJJ is one of the well-known crowding events that takes place every year in Makkah,Saudi Arabia.Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence(AI)applications.The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification(SSODTL-CD2C)model.The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities.To attain this,SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm(OSSA)with EfficientNet model to derive the feature vectors.At the same time,Stacked Sparse Auto Encoder(SSAE)model is utilized for the classification of crowd densities.Finally,SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism.The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities.The obtained results demonstrated that the proposed SSODTLCD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%.So,the proposed method will be highly helpful in managing HAJJ and other crowded events.

Biometric Verification System UsingHyperparameter Tuned Deep Learning Model

Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent years.These models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security systems.DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems.Iris recognition was considered the more reliable and accurate biometric detection method accessible.Iris recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border control.This article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)model.The presented PODTL-BIR technique recognizes the iris for biometric security.In the presented PODTL-BIR model,an initial stage of pre-processing is carried out.In addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature vectors.The PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric verification.Finally,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition efficiency.Awide-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR system.The experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Recent advancements in the Internet of Things(Io),5G networks,and cloud computing(CC)have led to the development of Human-centric IoT(HIoT)applications that transform human physical monitoring based on machine monitoring.The HIoT systems find use in several applications such as smart cities,healthcare,transportation,etc.Besides,the HIoT system and explainable artificial intelligence(XAI)tools can be deployed in the healthcare sector for effective decision-making.The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage.This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification(QIDEXAI-CDC)model for HIoT systems.The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems.The QIDEXAI-CDC model primarily uses bilateral filtering(BF)as a preprocessing tool to eradicate the noise.In addition,RetinaNet is applied for the generation of useful feature vectors from radiological images.For COVID-19 detection and classification,quantum-inspired differential evolution(QIDE)with kernel extreme learning machine(KELM)model is utilized.The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model.In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model,a wide range of simulations was carried out.Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches.

Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world.Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images.This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus Images.The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method.In the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus image.Besides,the DLLSHDM-DR applies HybridNet for producing a collection of feature vectors.For retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our work.To assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS dataset.The comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.

Group Action on Fuzzy Modules

In this article, we introduce the notion of fuzzy G-module by defining the group action of G on a fuzzy set of a Z-module M. We establish the cases in which fuzzy submodules also become fuzzy G-submodules. Notions of a fuzzy prime submodule, fuzzy prime G-submodule, fuzzy semi prime submodule, fuzzy G-semi prime submodule, G-invariant fuzzy submodule and G-invariant fuzzy prime submodule of M are introduced and their properties are described. The homomorphic image and pre-image of fuzzy G-submodules, G-invariant fuzzy submodules and G-invariant fuzzy prime submodules of M are also established.

A Novel Technique for Early Detection of COVID-19

COVID-19 is a global pandemic disease,which results from a dangerous coronavirus attack,and spreads aggressively through close contacts with infected people and artifacts.So far,there is not any prescribed line of treatment for COVID-19 patients.Measures to control the disease are very limited,partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease.Early detection of positive cases is critical in preventing further spread,achieving the herd immunity,and saving lives.Unfortunately,so far we do not have effective toolkits to diagnose very early detection of the disease.Recent research findings have suggested that radiology images,such as X-rays,contain significant information to detect the presence of COVID-19 virus in early stages.However,to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible.Artificial Intelligence(AI)techniques,machine learning in particular,are known to be very helpful in accurately diagnosing many diseases from radiology images.This paper proposes an automatic technique to classify COVID-19 patients from their computerized tomography(CT)scan images.The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network(AIRRCNN),which uses machine learning techniques for classifying data.We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network,because we do not find it being used in the literature.Also,we conduct principal component analysis,which is used for dimensional deduction.Experimental results of our method have demonstrated an accuracy of about 99%,which is regarded to be very efficient.

Requirements Analysis and Traceability at CIM Level

Poernomo suggested an approach for requirement analysis within the CIM level of the MDA framework. His approach combined MEASUR, goal and object oriented analysis, and developed a new methodology that can be integrated within the CIM level of the MDA. This paper adds requirement traceability capabilities to the method developed by Poernomo and applies the extended method on a case study based on a high profile international law firm.