RETRACTED ARTICLE: An automated exploring and learning model for data prediction using balanced CA-SVM

Neelakandan, S.; Paulraj, D.

doi:10.1007/s12652-020-01937-9

Cited by 83 publications

(18 citation statements)

References 22 publications

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“…However, the experimental analysis shows that the L-AKM consumes more transmission delay and throughput rate due to extra authentication phases involved in the working process of this scheme. Khalid et al [24,25] introduced a Light-Weight Structure-based Data Aggregation Routing (LSDAR) protocol for IoT integrated Next-generation Sensor Networks for the improvement of energy routing performance with data protection against malicious threats. Firstly, the network nodes were decomposed into independent clusters based on varying radii and preventing energy holes around the locality of the Base Station (BS).…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Key Management System Based Lightweight Devices in IoT

Priyadharshini¹,

Geetha²

2022

Intelligent Automation &Amp; Soft Computing

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The Internet of Things (IoT) has changed our lives significantly. Although IoT provides new opportunities, security remains a key concern while providing various services. Existing research methodologies try to solve the security and time-consuming problem also exists. To solve those problems, this paper proposed a Hashed Advanced Encryption Standard (HAES) algorithm based efficient key management system for internet-based lightweight devices in IoT networks. The proposed method is mainly divided into two phases namely Data Owner (DO) and Data User (DU) phase. The DO phase consists of two processes namely authentication and secure data uploading. In authentication, the registration process consists of three phases namely KGC, SCC and DVC. In Key Generation Center (KGC), the device ID is converted into a 128-bit hash key using the Point on Curve based Fowler-Noll-Vo (PoC-FNV) algorithm. In DVC, the Data Access Policy (DAP) is created by using the selected attributes which is in turn selected by using the Chen Chaotic Chimp Optimization Algorithm(CCCOA). Thus, the authentication information is stored in the blockchain. If the person is an authorized, then the data is securely stored and uploaded into the cloud server using HAES. In the DU phase, the DU sends the data access request to the blockchain then the blockchain forwards the request to the DO, if the DO accepts the request then the DU obtains the data by using the DAP. Finally, the performance of the proposed method is compared with the existing methods and attains better result than the existing research methodologies.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient Key Management System Based Lightweight Devices in IoT

Priyadharshini¹,

Geetha²

2022

Intelligent Automation &Amp; Soft Computing

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“…However, the combination of these modalities has been established effective at performing various processes on multi modal data. For instance, [11] integrated RGB and motion features extracted using CNN for detecting events and established an optimum efficiency was attained related to other networks with one modality. This paper presents a new vision based elderly fall event detection using deep learning (VEFED-DL) model.…”

Section: Introductionmentioning

confidence: 99%

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

Priya¹

2022

Computer Systems Science and Engineering

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Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based elderly fall event detection using deep learning (VEFED-DL) model. The proposed VEFED-DL model involves different stages of operations namely preprocessing, feature extraction, classification, and parameter optimization. Primarily, the digital video camera is used to capture the RGB color images and the video is extracted into a set of frames. For improving the image quality and eliminate noise, the frames are processed in three levels namely resizing, augmentation, and min-max based normalization. Besides, MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed frames. In addition, the extracted spatial features are then fed into the gated recurrent unit (GRU) to extract the temporal dependencies of the human movements. Finally, a group teaching optimization algorithm (GTOA) with stacked autoencoder (SAE) is used as a binary classification model to determine the existence of fall or non-fall events. The GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be enhanced. In order to assess the fall detection performance of the presented VEFED-DL model, a set of simulations take place on the UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the superior performance of the presented method over the recent methods.

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“…Various alternatives to the traffic jam crisis in the smart cities have been suggested. In order to reduce congestion, certain strategies propose the best individual routes [7], which may exacerbate congestion in other regions [8]. Others have proposed some classification algorithms, say, Fuzzy Neural Network (FNN), Random forest, C4.5, K-Nearest Neighbour (KNN), etc [9].…”

Section: Introductionmentioning

confidence: 99%

IoT-based traffic prediction and traffic signal control system for smart city

Neelakandan¹,

Berlin

Tripathi

et al. 2021

Soft Comput

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Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80286 microprocessor for a smart city.The proposed system consists of '5' phases, namely, IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data is collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80286 microprocessor. The experimental results show that the proposed system outperforms state-of-the-art methods.

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RETRACTED ARTICLE: An automated exploring and learning model for data prediction using balanced CA-SVM

Cited by 83 publications

References 22 publications

Efficient Key Management System Based Lightweight Devices in IoT

Efficient Key Management System Based Lightweight Devices in IoT

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

IoT-based traffic prediction and traffic signal control system for smart city

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