Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

Anupama, C. S. S.; Natrayan, L.; Lydia, E. Laxmi; Sait, Abdul Rahaman Wahab; Jos�Escorcia-Gutierrez,; Gamarra, Margarita; Mansour, Romany F.

doi:10.32604/cmc.2022.018396

Cited by 73 publications

(25 citation statements)

References 30 publications

(23 reference statements)

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“…The monitoring device, medical records, sensor, actuator, pharmacy control, and so on are consisted in the things layer [ 21 ]. The user of the ecosystem is directly connected to this layer.…”

Section: Methodsmentioning

confidence: 99%

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Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery

Sugadev

Rayen²,

Harirajkumar³

et al. 2022

Computational Intelligence and Neuroscience

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In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.

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Section: Methodsmentioning

confidence: 99%

“…e monitoring device, medical records, sensor, actuator, pharmacy control, and so on are consisted in the things layer [21].…”

Section: Ings Layermentioning

confidence: 99%

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery

Sugadev

Rayen²,

Harirajkumar³

et al. 2022

Computational Intelligence and Neuroscience

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“…ese filters are useful in the removal of salt and pepper noise from the images. In general, the spatial filter removes the noise from the images at the expense of losing the edge information from the image [9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 9: Various method results with 30% noise density: (a) Image with noise density of 30%; (b) MF[8]; (c) AMF[9]; (d) IQR[15]; (e) AIQR[16]; (f ) BPDF[18]; (g) NAFSM[13]; and (h) EBD.…”

mentioning

confidence: 99%

A Novel Effective Edge-Based Image Denoising Algorithm

Kumar¹,

Kumar²,

Naik³

et al. 2022

Scientific Programming

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This article proposes an edge-based denoising algorithm to restore the original image, which is highly degraded by the salt and pepper noise. Most of the existing image denoising algorithms consider edge as a noise. Here, the proposed algorithm can set out to resolve this ambiguity. The concept of directional filters is being used to delineate the edges from noise. The proposed algorithm performance is tested for different noise densities ranging from 5% to 90% on both the greyscale and colour images. It is compared with the current state of art techniques using several performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) values, and image enhancement factor (IEF). The results showed that the proposed algorithm has achieved an improvement of 60% over the state of art techniques.

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“…As of now, while a rice illness episode arises at various places, rice ailment experts of various agribusiness research or horticulture authorities see the spot and give guidance to the ranchers. At numerous places, there has not been satisfactory rice ailment pros contrasted with the quantity of ranchers [6]. ere has been an incredible requirement for programmed rice illness recognition utilizing effectively accessible gadgets in rustic zones.…”

Section: Introductionmentioning

confidence: 99%

Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business

Aggarwal

Suchithra

Chandramouli³

et al. 2022

Scientific Programming

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Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural innovation is one of them. The apparatuses which utilize the strategies of advanced artificial intelligence and machine learning have been featured in this paper. These techniques attain abnormally productive outcomes for the recognition of infections engrossing the images of leaves, fields of harvest, or seeds. In this context, this work presents a survey that focuses on accuracy agribusiness for expanding the conception of rice, which is one of the main harvests on the planet. In this paper, the overview and examination of various papers distributed in the most recent eight years with various methodologies identified with crop diseases identification, the health of seedlings, and quality of grain have been introduced. Experiments are performed for knowledge extraction using Web of Science and Scopus databases to analyze research trends in the domain of rice disease identification using artificial intelligence using global analysis, year-wise and country-wise citations, and so on to support various researchers working in this domain.

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

Cited by 73 publications

References 30 publications

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery

A Novel Effective Edge-Based Image Denoising Algorithm

Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business

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