A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai, G.; Poovammal, E.; Dhiman, Gaurav; Ramana, Kadiyala; Sharma, Ashutosh; AlZain, Mohammed A.; Gaba, Gurjot Singh; Masud, Mehedi

doi:10.1155/2021/5583874

Cited by 49 publications

(18 citation statements)

References 59 publications

(83 reference statements)

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“…Currently, various artificial intelligence (AI) techniques are under exploration to solve the ambiguity in diagnosis (16)(17)(18)(19)(20)(21)(22)(23). The existing AI techniques often require large data from a single source to train the deep learning model for a more accurate prediction.…”

Section: Introductionmentioning

confidence: 99%

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

Durga

Poovammal

2022

Front. Public Health

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With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

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Section: Introductionmentioning

confidence: 99%

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

Durga

Poovammal

2022

Front. Public Health

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“…For a known period of time, researchers are working on automobile traffic prediction and proposed few models to achieve it. In specialized publications, various solutions have been proposed [5] which function in a fair and accurate manner under regular circumstances i.e., no unplanned incidents on road network. But, on many occasions, these methodologies are unprepared to find out the convoluted congestion propagation patterns.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Hamza¹,

Alsolai²,

Alamgeer³

et al. 2022

Computers, Materials &Amp; Continua

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Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning (DL) is a kind of ML technique which yields effective performance on data classification and prediction tasks. With this motivation, the current study introduces a novel Slime Mould Optimization (SMO) model with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, data preprocessing is performed to normalize the input data in the range of [0, 1] using minmax normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Moreover, the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method. The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model's superior performance in terms of prediction compared to existing techniques.

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“…The researchers Cornell Tech identified and described the blockchain denial of service attack on the decentralised network on ACM 2020 SIGSAC Conference. The Distributed Denial of Service attacks conduces to target the web servers of the private organisations [21]. The DoS attacker blasted the servers with heavy spam traffic.…”

Section: Introductionmentioning

confidence: 99%

Calibration of chaos medichain by non-functional testing approaches

Durga

Poovammal

2022

ijhs

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The evolution of blockchain networks extends with the proliferation of the Internet of Medical Things devices. These devices require stupendous storage space which enlarges the maintenance cost. It raises undermined data security & Privacy, risk of failure, dishonest interlopes access to the centralized system. The propitious solution against these challenges of an extensive centralized system is decentralized-based blockchain technology. This paper implements decentralized-based blockchain architecture deployed with chaotic encryption and proof of consensus mechanism for mining. The disintermediate architecture as an enchanting target for Denial of Service (DoS) attacks. To covenant the trust, testers must certify that all the components of the blockchain network are employing flawlessly. The performance of non-functional testing assured the quality assurance services is a decisive aspect of the proposed blockchain architecture. The proposed decentralized-based blockchain framework is calibrated by injecting various malicious attacks. Moreover, the attacks are injected with other encryption-based blockchains to correlate the high effective scheme for medical data security. The evaluation reveals that the Proposed chaos medicine architecture shows 14% lower latency than SHA and 18 % lower latency than AES on all attacks and deliberate to be safe from malicious attack.

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A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Cited by 49 publications

References 59 publications

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Calibration of chaos medichain by non-functional testing approaches

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