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.

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

Spin Supercurrent in Phenomena of Quantum Non-Locality (Quantum Correlations, Magnetic Vector Potential) and in Near-Field Antenna Effect

It is shown that such phenomena as quantum correlations (interaction of space-separated quantum entities), the action of magnetic vector potential on quantum entities in the absence of magnetic field, and near-field antenna effect (the existence of superluminally propagating electromagnetic fields) may be explained by action of spin supercurrents. In case of quantum correlations between quantum entities, spin supercurrent emerges between virtual particles pairs (virtual photons) created by those quantum entities. The explanation of magnetic vector potential and near-field antenna effect is based on contemporary principle of quantum mechanics: the physical vacuum is not an empty space but the ground state of the field consisting of quantum harmonic oscillators (QHOs) characterized by zero-point energy. Using the properties of the oscillators and spin supercurrent, it is proved that magnetic vector potential is proportional to the moment causing the orientation of spin of QHO along the direction of magnetic field. The near-field antenna effect is supposed to take place as a result of action of spin supercurrent causing secondary electromagnetic oscillations. In this way, the electromagnetic field may spread at the speed of spin supercurrent. As spin supercurrent is an inertia free process, its speed may be greater than that of light, which does not contradict postulates of special relativity that sets limits to the speed of inertial systems only.

An Analogy between the Properties of “Dark Energy” and Physical Vacuum Consisting of Quantum Harmonic Oscillators Characterized by Zero-Point Energy

In quantum field theory, the physical vacuum, free from magnetic and electric fields (without regard to gravitational energy), is defined not as an empty space but as the ground state of the field consisting of quantum harmonic oscillators (QHOs) characterized by zero-point energy. The aim of this work is to show that such physical vacuum may possess the properties similar to the properties of dark energy: the positive density, the negative pressure, and the possibility of so-called accelerated expansion. In the model discussed, the mass of QHOs determines the positive density of dark energy. The observed electric polarization of physical vacuum in an electric field means the existence of electric dipole moment of QHO, which, in turn, suggests the existence inside the QHO of a repulsive force between unlike charges compensating the attractive Coulomb force between the charges. The existence of such repulsive force may be treated as the existence of omniradial tensions inside every QHO. In terms of hydrodynamics, it means that the vacuum with this property may be regarded as a medium with negative pressure. The electric dipole-dipole interaction of QHOs under some condition may result in the expansion of physical vacuum consisting of QHOs. It is shown also that the physical vacuum consisting of QHOs is a luminiferous medium, and based on this concept the conditions are discussed for the emergence of invisiblity of any objects (in particular, dark matter). The existence of luminiferous medium does not contradict the second postulate of special relativity (the principle of constancy of the velocity of light in inertial systems), if to take into account the interaction of photons with QHOs and with virtual photons (the virtual particles pairs) created by quantum entities that constitute the inertial systems.

The Big Eurasian Partnership as a Model of a New World Order:from the American to the Asian World Economic Paradigm

The paper applies the conception of world economic and technological modes’changing in order to justify the emergence of a new model of global economic order.The research is focused on the justification of the Big Eurasian Partnership(BEP)as such kind of pattern.We’ve started to form the basis and create a Road Map for the BEP building.First of all,we determine the conditions for the BEP construction taking into account the logic of switching from the American(Imperial)to the Asian(Integral)world economic paradigm.The paper also includes clear author’s definition of the BEP and the formulations of the goals for its creation.The author puts forward the principles of the Big Eurasian Partnership,reveals the active zones of conjugation processes in the space of the Big Eurasia and highlights the positive effect from the interconnection of the countries within the BEP construction.It is also showed in the paper how different international initiatives uniting the countries(like“Belt and Road”initiative of China,Eurasian Economic Union,etc.)can co-exist within the common BEP concept.The paper contains the results of scientific diplomacy worked out by the author according to the general idea of Russian President.

Metaheuristic Clustering Protocol for Healthcare DataCollection in MobileWireless Multimedia Sensor Networks

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.

Optimal Confidential Mechanisms in Smart City Healthcare

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam optimization.The key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box models.The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies.

High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

Cloud computing offers internet location-based affordable,scalable,and independent services.Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic situations.To handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified configurations.Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server.Data deduplication also saves network bandwidth.In this paper,a new cloud-based big data security technique utilizing dual encryption is proposed.The clustering model is utilized to analyze the Deduplication process hash function.Multi kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption procedure.The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is used.This security process involving dual encryption with the optimization model develops the productivity mechanism.In this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques.The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for stroke.Images generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of hemorrhages.Radiologists use manual planimetry,a time-consuming process for segmenting CT scan images.Deep Learning(DL)is the most preferred method to increase the efficiency of diagnosing ICH.In this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as FFEDL-ICH.The proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and classification.The input image is first preprocessed using the Gaussian Filtering(GF)technique to remove noise.Secondly,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the images.Furthermore,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful features.Finally,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of ICH.The researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance.For future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis.The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials.Presently,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high performance.In this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC model.Initially,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier results.Next,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML map.In addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class labels.An extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University dataset.The obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information processing.The recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain standards.The restrictions and requirements in QoS management completely depend upon the nature of target application.Some of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and throughput.In this scenario,clustering and routing are considered as the most effective techniques to meet the demands of QoS.Since they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be implemented.The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)algorithm.The proposed QoSCRSI technique performs two-level clustering and proficient routing.Initially,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the network.In order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node counts.The experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.

An Optimal Classification Model for Rice Plant Disease Detection

Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.

AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directly in these big data streams.At the same time,streaming data from several applications results in two major problems such as class imbalance and concept drift.The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming Data.The presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and classification.MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is employed.Besides,Statistical Test of Equal Proportions(STEPD),a CDD technique is also utilized.Finally,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for classification.In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried out.The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam dataset.An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model.The proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely utilized.This article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise.In addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature subsets.Moreover,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial data.Furthermore,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model.For enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

AModified Search and Rescue Optimization Based Node Localization Technique inWSN

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so on.In spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of anchors.The efficiency of the WSN can be considerably influenced by the node localization accuracy.Therefore,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)forWSN.The major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in theWSN.Since the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique.The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm.In order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT

Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and efficiency.The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality.The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things(IoT)in cloud computing environment.The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques.The current research article presents a new Oppositional Glowworm Swarm Optimization(OGSO)algorithmbased clustering with Deep Neural Network(DNN)called OGSO-DNN model for distributed healthcare systems.The OGSO algorithm was applied in this study to select the Cluster Heads(CHs)from the available IoT devices.The selected CHs transmit the data to cloud server,which then executes DNN-based classification process for healthcare diagnosis.An extensive simulation analysis was carried out utilizing a student perspective healthcare data generated from UCI repository and IoT devices to forecast the severity level of the disease among students.The proposed OGSO-DNN model outperformed previous methods by attaining the maximum average sensitivity of 96.956%,specificity of 95.076%,the accuracy of 95.764%and F-score value of 96.888%.

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.