Implementation of Hidden Node Detection Scheme for Self-Organization of Data Packet

Panatula, Ganesh; Naidu, G. B. S. R.; Swaroopa, Korla; Rahul, R.; Almadhor, Ahmad; Senthilkumar, C.; Gangodkar, Durgaprasad; Rajaram, Anand; Yeshitla, Alazar

doi:10.1155/2022/1332373

Cited by 28 publications

(17 citation statements)

References 19 publications

(17 reference statements)

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“…Our proposed approach provides an enhanced Convolutional Neural Network for the classification of the whole scale images. And, finally, our proposed paper compares with the existing approach in the entire image processing system [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Proposed Methodologymentioning

confidence: 99%

Skin Diseases Classification Using Hybrid AI Based Localization Approach

Sreekala¹,

Rajkumar

Sugumar

et al. 2022

Computational Intelligence and Neuroscience

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One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. The researcher used a variety of strategies and methods to identify and stop diseases earlier. All of them yield positive results for identifying and categorizing diseases, but proper disease categorization is still lacking. Computer-aided diagnosis is one of the most crucial methods for more accurate disease detection, although it is rarely used in dermatology. For Feature Extraction, we introduced Spectral Centroid Magnitude (SCM). The given dataset is classified using an enhanced convolutional neural network; the first stage of preprocessing uses a median filter, and the final stage compares the accuracy results to the current method.

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Section: Proposed Methodologymentioning

confidence: 99%

Skin Diseases Classification Using Hybrid AI Based Localization Approach

Sreekala¹,

Rajkumar

Sugumar

et al. 2022

Computational Intelligence and Neuroscience

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“…e energy consumption, the response time, and the two other parameters are used to explain the QoS methodology of the entire network services [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Literature Reviewmentioning

confidence: 99%

QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach

Loganathan

Chitra²,

Sivasankari³

et al. 2022

Computational Intelligence and Neuroscience

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This work explains why and how QoS modeling has been used within a multicriteria optimization approach. The parameters and metrics defined are intended to provide a broader and, at the same time, more precise analysis of the issues highlighted in the work dedicated to placement algorithms in the cloud. In order to find the optimal solution to a placement problem which is impractical in polynomial time, as in more particular cases, meta-heuristics more or less approaching the optimal solution are used in order to obtain a satisfactory solution. First, a model by a genetic algorithm is proposed. This genetic algorithm dedicated to the problem of placing virtual machines in the cloud has been implemented in two different versions. The former only considers elementary services, while the latter uses compound services. These two versions of the genetic algorithm are presented, and also, two greedy algorithms, round-robin and best-fit sorted, were used in order to allow a comparison with the genetic algorithm. The characteristics of these two algorithms are presented.

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“…The multipath routing process must not recover the link failure. This minimizes the network lifetime and detection efficiency and improves the communication overhead [22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Overview Of Proposed Schemementioning

confidence: 99%

Energy Auditing and Broken Path Identification for Routing in Large-Scale Mobile Networks Using Machine Learning

Venu

Revanesh

Supriya³

et al. 2022

Wireless Communications and Mobile Computing

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In mobile computing, all nodes are movable nodes, which causes many problems for transmitting data packets in a sequence manner; since the mobile nodes are connected with each other, during movement, nodes make the connection fail or damaged. This kind of link damage is caused by nodes that travel out of range from the network limit and also affect the packet success rate. This reduces the network lifetime and detection efficiency and increases the communication overhead. Every mobile node in mobile computing is an unstable node, causing numerous problems for broadcasting data packets in a series method. When the mobile nodes are connected to each other, relay nodes cause the link to break or else sustain damage. This type of connection failure is brought on by nodes that leave the network’s permitted range, which also lowers the packet success rate. The link failure cannot be recovered by the multipath routing algorithm. As a result, the communication overhead is increased while the network lifetime and detection effectiveness are reduced. Then, the novel energy routing (NER) method that has been proposed is employed to support the energetic routing path across the middle nodes. It is challenging to locate the failed channels and carry on with the successful packet transfer. The master node selection algorithm is intended to identify the best relaying node, fault-free packet transmission process among the network structure’s relaying nodes. The master node is efficiently chosen in this manner. The master node, also known as the energy-based important node, is employed in the mobile network to carry out error-free packet transmission procedures. The other nodes are lower energy nodes that do not participate in packet forwarding, and this algorithm only detects the higher energy successful nodes. This lengthens the network’s lifespan, improves detection effectiveness, and lowers communication overhead. The energy-based heavy node is also known as the master node, which is used to perform a problem-free communication process in the mobile network. This algorithm only accepts the higher energy successful node; the remaining nodes are lower energy nodes which do not perform the communication process. This increases the network lifetime and detection efficiency and reduces the communication overhead. The performance metrics for the proposed system is evaluated by end to end delay, communication overhead, throughput, detection efficiency, network lifetime, and packet drop rate.

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Implementation of Hidden Node Detection Scheme for Self-Organization of Data Packet

Cited by 28 publications

References 19 publications

Skin Diseases Classification Using Hybrid AI Based Localization Approach

Skin Diseases Classification Using Hybrid AI Based Localization Approach

QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach

Energy Auditing and Broken Path Identification for Routing in Large-Scale Mobile Networks Using Machine Learning

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