Fitness adaptive deer hunting‐based region growing and recurrent neural network for melanoma skin cancer detection

Divya, D.; Ganeshbabu, T. R.

doi:10.1002/ima.22414

Cited by 20 publications

(7 citation statements)

References 35 publications

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“…This experimental analysis was undergone with a population count of 10 and maximum iterations of 25 for the proposed pest identification and classification model. The proposed AHBA-CNLSTM was compared with other meta-heuristic algorithms like "Particle Swarm Optimization (PSO) [27], Tunicate Swarm Algorithm (TSA) [28], Deer Hunting Optimization Algorithm (DHOA) [29], HBA [26] and deep learning algorithms like CNN [7], deep-CNN [6], RCNN [5] and LSTM [2]".…”

Section: Resultsmentioning

confidence: 99%

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Kathole¹,

Vhatkar²,

Patil³

2022

Cybernetics and Systems

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The insect pests and crop diseases are the most critical factors that affect agricultural production, which reduces the sustainable development of agriculture. While detecting the pest, it is inconsistent to place the surveillance cameras near the target pests and the captured images from the Internet of Things (IoT) monitoring equipment at a constant location that is mostly insufficient for pest detection. IoT is a well-known advanced technology and an analytics system incorporated in diverse industries based on its unique abilities and flexibilities over a particular environment like agriculture. There is a demand for the IoT in agricultural areas to reduce the chemical crop protection agents and fertilizers to manage the efficient crop state and crop production. Hence, the data collection is through the IoT devices in this research model. This research aims to develop a pest identification and classification model to detect and identify the pests in the images. Initially, the IoT platform is created, and IoT devices conduct the data collection. Then, object detection is performed using Yolov3 to detect the pests in the images from the gathered images. The detected images are subjected to the Convolutional Neural Network (CNN) for gathering the deep features, which are then forwarded to the enhanced classifier, termed Convolution Neural Long Short-Term Memory (CNLSTM) for getting the classified outcomes as pest details, in which the optimization of parameters is done by Adaptive Honey Badger Algorithm (AHBA). These results demonstrated that the proposed method shows enhanced performance by rapidly collecting the information in agriculture and ensures the technical indication for population estimation and pest monitoring.

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Section: Resultsmentioning

confidence: 99%

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Kathole¹,

Vhatkar²,

Patil³

2022

Cybernetics and Systems

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“…However, some patients may develop a variety of symptoms, including paresthesia, numbness, and neuropathic pain, which can be slightly bothersome to intractable and cause significant discomfort. (26,27) The occurrence of these symptoms and the length of time they last vary from patient to patient, as does their frequency. Sensory symptoms can last for a short time before going away, or they can last for a long time.…”

Section: Clinical Features Of Diabetic Neuropathymentioning

confidence: 99%

Evaluation and Management of Diabetic Neuropathy from the Perspective of People with Diabetes

Babu,

Roobini,

Prabhakaran

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Diabetic foot ulcers (DFU) and infections are the most common complications of diabetic foot disease. Mortality and financial burdens for both patients and society on the whole are caused by the prevalence of complications. Peripheral neuropathy, peripheral arterial disease, and immune response dysfunction are just a few of the main contributing factors that must be understood in order to effectively treat the condition. In order to treat diabetic foot disease, you must first get a comprehensive physical examination and a detailed history of your condition. Diabetic neuropathy and peripheral vascular disease, as well as any evidence of diabetic foot ulcers or infection, should be examined during this procedure. Patients with diabetes mellitus were studied to see if there was a link between cognitive impairment and the condition of their feet and whether or not they followed their doctor's recommendations for glycemic control. Using a random sample of diabetes patients, researchers conducted a prospective study to see how many people had the condition. The Mini-Mental State Valuation, the Trail Making Judgments, and the Michigan Screening Instrument were used to assess cognitive abilities. In the one-month follow-up, glycated hemoglobin (HB1Ac >7%) was linked to the MMSE and medication adherence, but no link was seen between cognitive function and neuropathy. According to the results of a ROC curve investigation, HB1Ac and the MNSI score both significantly (p< 0.05) mitigate towards eventual adherence to medicine for foot problems. For the purpose of determining if DFU was associated with cognitive impairment, the Chi square valuation was used in the statistical examination. As a determinant of MMSE and MoCA scores, the researchers used linear regression to come to their conclusion. Diabetic foot issues should be managed with good blood sugar control and less acute neuropathy, and this does not seem to be linked to cognitive dysfunction. More study is required in order to personalize treatments for diseases of the central and peripheral nervous systems appropriately. Cognitive dysfunction should be taken into account by doctors and podiatrists while treating diabetic foot problems

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“…This system attains a higher accuracy of 90.48% on ISIC 2020. 60 proposes a new architecture using a deep learning approach for skin cancer diagnosis. This model contains four stages: preprocessing which includes image enhancement and hair removal, segmentation by an optimized region growing algorithm, feature extraction, and finally recurrent neural networks (RNNs) are combined with the optimization concept to create a modified deep learning algorithm for classification.…”

Section: Deep Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

Skin cancer diagnosis using artificial intelligence methods: a review of the most recent research

dahdouh,

Abdelhakim Boudhir,

Ben Ahmed

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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Fitness adaptive deer hunting‐based region growing and recurrent neural network for melanoma skin cancer detection

Cited by 20 publications

References 35 publications

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Evaluation and Management of Diabetic Neuropathy from the Perspective of People with Diabetes

Skin cancer diagnosis using artificial intelligence methods: a review of the most recent research

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