Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.

Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users.It encompasses several heterogeneous resource and communication standard in ensuring incessant availability of service.At the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,etc.In UAV networks,energy efficiency and data collection are considered the major process for high quality network communication.But these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted UAVs.These issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G environment.With this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G environment.The proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets minimized.The presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct clusters.The QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of UAVs.The performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

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.

Deep Learning Based License Plate Number Recognition for Smart Cities

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

Wireless sensor networks(WSN)encompass a set of inexpensive and battery powered sensor nodes,commonly employed for data gathering and tracking applications.Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination.The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network.In this aspect,this paper presents an enhanced Archimedes optimization based cluster head selection(EAOA-CHS)approach for WSN.The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters.Besides,the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance.Moreover,the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN.The design of EAOA for CH election in the WSN depicts the novelty of work.In order to exhibit the enhanced efficiency of EAOA-CHS technique,a set of simulations are applied on 3 distinct conditions dependent upon the place of base station(BS).The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model

Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.

Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD.Recognition of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were affected.Nowadays,healthcare and machine learning(ML)industries are combined for determining the existence of various diseases.This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)model.The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data.The proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform range.In addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature subsets.Moreover,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ASD.Furthermore,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU system.The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets.The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

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%.

Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,etc.Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks.This paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT environments.The WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data transmission.Initially,the medical images were captured from the patient using IoT devices.Then,the captured images are encrypted using signcryption technique.In addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA algorithm.The goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain securely.The performance of the presented method was analyzed utilizing the benchmark medical image dataset.The security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended user.With the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target domain.The existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia content.Since energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network lifetime.With this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for WMSN.The proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum computing.The proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance measures.The simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the grid.The classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the grids.The TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them.At the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)models.Though some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,etc.into account.In this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in TEM.The proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence rate.Moreover,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading systems.In order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome.The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death rate.Histopathological imaging is a wide-spread standard used for OSCC detection.However,it is a cumbersome process and demands expert’s knowledge.So,there is a need exists for automated detection ofOSCC using Artificial Intelligence(AI)and Computer Vision(CV)technologies.In this background,the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification(ISMA-AIOCC)model on Histopathological images(HIs).The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs.At the initial stage,linear smoothing filter is applied to eradicate the noise from images.Besides,MobileNet model is employed to generate a useful set of feature vectors.Then,Bidirectional Gated Recurrent Unit(BGRU)model is exploited for classification process.At the end,ISMA algorithm is utilized to fine tune the parameters involved in BGRU model.Moreover,ISMA algorithm is created by integrating traditional SMA and ChaoticOppositional Based Learning(COBL).The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based application.The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor nodes.The clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems.With this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted WSN.The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN environment.In addition,an effective multihop route planning approach was designed by the use of EHGS technique.The proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS algorithm.Moreover,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal routes.The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

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.

Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

Recently,big data becomes evitable due to massive increase in the generation of data in real time application.Presently,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,etc.This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data environment.The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT model.The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast R-CNN.In addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature map.Lastly,softmax layer is applied to perform classification task.The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

Presently,customer retention is essential for reducing customer churn in telecommunication industry.Customer churn prediction(CCP)is important to predict the possibility of customer retention in the quality of services.Since risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer behavior.Besides,deep learning(DL)models help in prediction of the customer behavior based characteristic data.Since the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business people.In this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application sector.In addition,the O-DCCAEP method purposes for determining the churning nature of the customers.The O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter optimization.Additionally,the DCCAE model is employed to classify the churners or non-churner.Furthermore,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.