Beware the Black Widow at Claim Time: A Report of Three Cases.

Moral hazard is well known to life insurance underwriters and medical directors to increase the risk of adverse consequences to insured individuals. The underwriting investigation of proposed insureds at time of policy issue is done to ensure no likely moral hazard exists. However, not all situations involving moral hazard may be identified at time of underwriting and policy issue, and may only be identified at time of claim. Three cases that were underwritten for life expectancies in legal matters are described here as examples of moral hazard identified at time of severe injury and/or death. All three of these cases involved a woman who manipulated her male partner into situations that increased the man's risk of severe injury and/or death to the woman's financial benefit. Such "black widows" made a great deal of effort over an extensive period of time to ensure that the moral hazard set up for their male partners resulted in a substantial financial windfall through litigation. The moral hazard set up by a black widow thus can be considered by the life insurance industry as sufficiently anti-selective and speculative to deny a claim at any time after policy issue.

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system.

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.

Quantitative Shotgun Proteomic Analysis of Bacteria after Overexpression of Recombinant Spider Miniature Spidroin, MaSp1.

Spider silk has extraordinary mechanical properties, displaying high tensile strength, elasticity, and toughness. Given the high performance of natural fibers, one of the long-term goals of the silk community is to manufacture large-scale synthetic spider silk. This process requires vast quantities of recombinant proteins for wet-spinning applications. Attempts to synthesize large amounts of native size recombinant spidroins in diverse cell types have been unsuccessful. In these studies, we design and express recombinant miniature black widow MaSp1 spidroins in bacteria that incorporate the N-terminal and C-terminal domain (NTD and CTD), along with varying numbers of codon-optimized internal block repeats. Following spidroin overexpression, we perform quantitative analysis of the bacterial proteome to identify proteins associated with spidroin synthesis. Liquid chromatography with tandem mass spectrometry (LC MS/MS) reveals a list of molecular targets that are differentially expressed after enforced mini-spidroin production. This list included proteins involved in energy management, proteostasis, translation, cell wall biosynthesis, and oxidative stress. Taken together, the purpose of this study was to identify genes within the genome of Escherichia coli for molecular targeting to overcome bottlenecks that throttle spidroin overexpression in microorganisms.

First winter record of Latrodectus mirabilis (Araneae: Theridiidae) in arid Patagonia: A consequence of climate change and urbanization?

Cases of araneism reported in the province of Chubut (Argentina) have tripled in the last two decades, and almost 80 % of them involve Latrodectus mirabilis (Holmberg) (Araneae: Theridiidae). According to descriptions of the life cycle of this species in Argentina, the low temperatures typical of autumn-winter cause the death of all adult spiders, so that no adult specimens of L. mirabilis are observed in winter. Field samplings, observations by the Grupo de Entomología Patagónica (GENTPAT, IPEEC CCT CENPAT CONICET), and citizen reports for more than 15 years suggested a similar cycle in northeastern Patagonia. However, for the last three consecutive years, we have recorded adult females in the field throughout the Patagonian winter. Some of these individuals even survived the winter and were alive the following spring. The purpose of this note is to report the field presence of adult female specimens of L. mirabilis in the outskirts of the city of Puerto Madryn (Chubut, Argentina) during the last three consecutive winters corresponding to the years 2021, 2022 and 2023; and to note that at least two of them survived the winter, arriving alive (and in good condition) the following spring. Given the medical importance of this spider, the publication of this information, the context of the findings, and their ecological implications will help to prevent its spread and reduce the likelihood of accidents.

The functional microclimate of an urban arthropod pest: Urban heat island temperatures in webs of the western black widow spider.

Urbanization alters natural landscapes and creates unique challenges for urban wildlife. Similarly, the Urban Heat Island (UHI) effect can produce significantly elevated temperatures in urban areas, and we have a relatively poor understanding of how this will impact urban biodiversity. In particular, most studies quantify the UHI using broad-scale climate data rather than assessing microclimate temperatures actually experienced by organisms. In addition, studies often fail to address spatial and temporal complexities of the UHI. Here we examine the thermal microclimate and UHI experienced in the web of Western black widow spiders (Latrodectus hesperus), a medically-important, superabundant urban pest species found in cities across the Western region of North America. We do this using replicate urban and desert populations across an entire year to account for seasonal variation in the UHI, both within and between habitats. Our findings reveal a strong nighttime, but no daytime, UHI effect, with urban spider webs being 2-5 °C warmer than desert webs at night. This UHI effect is most prominent during the spring and least prominent in winter, suggesting that the UHI need not be most pronounced when temperatures are most elevated. Urban web temperatures varied among urban sites in the daytime, whereas desert web temperatures varied among desert sites in the nighttime. Finally, web temperature was significantly positively correlated with a spider's boldness, but showed no relationship with voracity towards prey, web size, or body condition. Understanding the complexities of each organism's thermal challenges, the "functional microclimate", is crucial for predicting the impacts of urbanization and climate change on urban biodiversity and ecosystem functioning.

Latrodectus envenomation in Ethiopia.

Black widows, one of the few spiders that can sting humans with poison, are members of the spider genus Latrodectus and are well-known for the extraordinary potency of their neurotoxic venom. Latrodectism, a symptom marked by excruciating muscular pain, stomach pain, and diaphoresis after envenomation, is very typical. We described a black widow envenomation case that produced a significant reaction, including diaphoresis and excruciating pain throughout the left thigh that later spread to the lower leg, lower back, belly, and chest. Because of the patient's description of the spider that bit him and his typical clinical state, it was assumed that Latrodectus envenomation was the cause of his symptoms. The patient received 3 days of observation in the ED while receiving opioid analgesic pain management and muscle relaxant treatment with diazepam. The patient's pain and symptoms were satisfactorily managed, and he was sent home. This case report will help further research be done in the area where it was reported to see if there are cases with similar presentations misdiagnosed as other illnesses. Finally, immediate pain relief is the most critical goal for all patients.

Improving the prediction of wildfire susceptibility on Hawai'i Island, Hawai'i, using explainable hybrid machine learning models.

This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawai'i Island, Hawai'i. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawai'i Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems.

Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. The complexities of GS are especially noticeable in the context of microarray expression data analysis, owing to the inherent data imbalance. The main goal of this study is to offer a simplified and computationally effective approach to dealing with the conundrum of attribute selection in microarray gene expression data. We use the Black Widow Optimization algorithm (BWO) in the context of GS to achieve this, using two unique methodologies: the unaltered BWO variation and the hybridized BWO variant combined with the Iterated Greedy algorithm (BWO-IG). By improving the local search capabilities of BWO, this hybridization attempts to promote more efficient gene selection. A series of tests was carried out using nine benchmark datasets that were obtained from the gene expression data repository in the pursuit of empirical validation. The results of these tests conclusively show that the BWO-IG technique performs better than the traditional BWO algorithm. Notably, the hybridized BWO-IG technique excels in the efficiency of local searches, making it easier to identify relevant genes and producing findings with higher levels of reliability in terms of accuracy and the degree of gene pruning. Additionally, a comparison analysis is done against five modern wrapper Feature Selection (FS) methodologies, namely BIMFOHHO, BMFO, BHHO, BCS, and BBA, in order to put the suggested BWO-IG method's effectiveness into context. The comparison that follows highlights BWO-IG's obvious superiority in reducing the number of selected genes while also obtaining remarkably high classification accuracy. The key findings were an average classification accuracy of 94.426, average fitness values of 0.061, and an average number of selected genes of 2933.767.

Applying dual models on optimized LSTM with U-net segmentation for breast cancer diagnosis using mammogram images.

BACKGROUND OF THE STUDY: Breast cancer is the most fatal disease that widely affects women. When the cancerous lumps grow from the cells of the breast, it causes breast cancer. Self-analysis and regular medical check-ups help for detecting the disease earlier and enhance the survival rate. Hence, an automated breast cancer detection system in mammograms can assist clinicians in the patient's treatment. In medical techniques, the categorization of breast cancer becomes challenging for investigators and researchers. The advancement in deep learning approaches has established more attention to their advantages to medical imaging issues, especially for breast cancer detection. AIM: The research work plans to develop a novel hybrid model for breast cancer diagnosis with the support of optimized deep-learning architecture. METHODS: The required images are gathered from the benchmark datasets. These collected datasets are used in three pre-processing approaches like "Median Filtering, Histogram Equalization, and morphological operation", which helps to remove unwanted regions from the images. Then, the pre-processed images are applied to the Optimized U-net-based tumor segmentation phase for obtaining accurate segmented results along with the optimization of certain parameters in U-Net by employing "Adapted-Black Widow Optimization (A-BWO)". Further, the detection is performed in two different ways that is given as model 1 and model 2. In model 1, the segmented tumors are used to extract the significant patterns with the help of the "Gray-Level Co-occurrence Matrix (GLCM) and Local Gradient pattern (LGP)". Further, these extracted patterns are utilized in the "Dual Model accessed Optimized Long Short-Term Memory (DM-OLSTM)" for performing breast cancer detection and the detected score 1 is obtained. In model 2, the same segmented tumors are given into the different variants of CNN, such as "VGG19, Resnet150, and Inception". The extracted deep features from three CNN-based approaches are fused to form a single set of deep features. These fused deep features are inserted into the developed DM-OLSTM for getting the detected score 2 for breast cancer diagnosis. In the final phase of the hybrid model, the score 1 and score 2 obtained from model 1 and model 2 are averaged to get the final detection output. RESULTS: The accuracy and F1-score of the offered DM-OLSTM model are achieved at 96 % and 95 %. CONCLUSION: Experimental analysis proves that the recommended methodology achieves better performance by analyzing with the benchmark dataset. Hence, the designed model is helpful for detecting breast cancer in real-time applications.

Reversible Myocarditis Following Black Widow Spider (Latrodectus spp.) Bite in Egypt: A case report.

Black widow spiders (BWSs) are poisonous spiders of the Arthropoda phylum that live in the Mediterranean region. The effects of BWS bites ranges from local damage to systemic manifestations including paresthesia, stiffness, abdominal cramps, nausea, vomiting, headache, anxiety, hypertension and tachycardia. However, cardiac involvement following a BWS bite is uncommon. We report a 35-year-old male patient who presented to a tertiary hospital in Menoufia, Egypt, in 2019 and developed acute pulmonary oedema with electrocardiogram (ECG) changes that showed ST elevation in leads I and aVL with reciprocal ST segment depression in infero-lateral leads with elevated cardiac biomarkers. Echocardiography showed regional wall motion abnormalities with an impaired ejection fraction of 42%. The condition was reversible after one week of supportive treatment and the patient was discharged from the hospital with normal electrocardiogram, ejection fraction and negative cardiac markers. A routine cardiac evaluation, serial ECG, serial cardiac markers and echocardiography should be considered for any patient exposed to a BWS bite for detection of any potentially fatal cardiac abnormalities.

A Novel Cipher-Based Data Encryption with Galois Field Theory.

Both the act of keeping information secret and the research on how to achieve it are included in the broad category of cryptography. When people refer to "information security," they are referring to the study and use of methods that make data transfers harder to intercept. When we talk about "information security," this is what we have in mind. Using private keys to encrypt and decode messages is a part of this procedure. Because of its vital role in modern information theory, computer security, and engineering, cryptography is now considered to be a branch of both mathematics and computer science. Because of its mathematical properties, the Galois field may be used to encrypt and decode information, making it relevant to the subject of cryptography. The ability to encrypt and decode information is one such use. In this case, the data may be encoded as a Galois vector, and the scrambling process could include the application of mathematical operations that involve an inverse. While this method is unsafe when used on its own, it forms the foundation for secure symmetric algorithms like AES and DES when combined with other bit shuffling methods. A two-by-two encryption matrix is used to protect the two data streams, each of which contains 25 bits of binary information which is included in the proposed work. Each cell in the matrix represents an irreducible polynomial of degree 6. Fine-tuning the values of the bits that make up each of the two 25-bit binary data streams using the Discrete Cosine Transform (DCT) with the Advanced Encryption Standard (AES) Method yields two polynomials of degree 6. Optimization is carried out using the Black Widow Optimization technique is used to tune the key generation in the cryptographic processing. By doing so, we can produce two polynomials of the same degree, which was our original aim. Users may also use cryptography to look for signs of tampering, such as whether a hacker obtained unauthorized access to a patient's medical records and made any changes to them. Cryptography also allows people to look for signs of tampering with data. Indeed, this is another use of cryptography. It also has the added value of allowing users to check for indications of data manipulation. Users may also positively identify faraway people and objects, which is especially useful for verifying a document's authenticity since it lessens the possibility that it was fabricated. The proposed work achieves higher accuracy of 97.24%, higher throughput of 93.47%, and a minimum decryption time of 0.0047 s.

BWOA assisted PIDF-(1+I) controller for intelligent load frequency management of standalone micro-grid.

The study made in this paper has been directed towards a novel load frequency management (LFM) scheme for solar-wind-based standalone micro-grid (SMG). For LFM, this brief deals with the introduction of proportional-integral-derivative with filter - (one plus integral), i.e., PIDF-(1+I) cascade controller. A maiden endeavor has been performed to employ a recently developed black widow optimization algorithm (BWOA) to obtain the supplementary controller parameters. The considered SMG consists of the wind turbine generator, diesel engine generator, solar photovoltaic as distributed generation unit, and flywheel and ultra-capacitor are considered as energy storage systems. Generation rate constraints and governor dead-band type power system's nonlinearities are also included in this study. This work aims to mitigate the effect of mismatch in demand and generation and minimize the change in frequency deviation (CFD). The maximum obtained CFD with the proposed controller is 0.048 Hz, which is entirely satisfactory and under the permissible limit of IEEE standard. A vivid comparative analysis of artificial bee colony and BWOA tuned controllers like conventional PID, PIDF, and PIDF-(1+I) is also performed. Finally, the detailed robustness assessment of the proposed controller with its real-time implementation through the standard New England IEEE 39 test bus system presents the controller's superiority.

Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm.

Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (dc). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of dc when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize dc for maximizing accuracy, achieving automatic determination of dc. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC.

Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies.

The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (IBWOA). First, Gauss chaotic mapping is introduced to initialize the population to ensure the diversity of the algorithm at the initial stage. Then, the sine cosine strategy is introduced to perturb the individuals during iteration to improve the global search ability of the algorithm. In addition, the elite opposition-based learning strategy is introduced to improve convergence speed of algorithm. Finally, the mutation method of the differential evolution algorithm is integrated to reorganize the individuals with poor fitness values. Through the analysis of the optimization results of 13 benchmark test functions and a part of CEC2017 test functions, the effectiveness and rationality of each improved strategy are verified. Moreover, it shows that the proposed algorithm has significant improvement in solution accuracy, performance and convergence speed compared with other algorithms. Furthermore, the IBWOA algorithm is used to solve six practical constrained engineering problems. The results show that the IBWOA has excellent optimization ability and scalability.

Validation of a Novel Stereo Vibrometry Technique for Spiderweb Signal Analysis.

From courtship rituals, to prey identification, to displays of rivalry, a spider's web vibrates with a symphony of information. Examining the modality of information being transmitted and how spiders interact with this information could lead to new understanding how spiders perceive the world around them through their webs, and new biological and engineering techniques that leverage this understanding. Spiders interact with their webs through a variety of body motions, including abdominal tremors, bounces, and limb jerks along threads of the web. These signals often create a large enough visual signature that the web vibrations can be analyzed using video vibrometry on high-speed video of the communication exchange. Using video vibrometry to examine these signals has numerous benefits over the conventional method of laser vibrometry, such as the ability to analyze three-dimensional vibrations and the ability to take measurements from anywhere in the web, including directly from the body of the spider itself. In this study, we developed a method of three-dimensional vibration analysis that combines video vibrometry with stereo vision, and verified this method against laser vibrometry on a black widow spiderweb that was experiencing rivalry signals from two female spiders.

North American Envenomation Syndromes.

This review discusses the distinct envenomation syndromes produced by North American species of snakes and arthropods, specifically the Crotalinae subfamily of snakes, which includes cottonmouths, copperheads, and rattlesnakes; coral snakes; Latrodectus and Loxosceles species of arachnid; and Centruroides sculpturatus, the only species of North American scorpion capable of producing an envenomation syndrome. The authors discuss the epidemiology, pathophysiology, and presentation of these syndromes and emphasize the varying degrees to which these syndromes can manifest clinically. Finally, the management of each envenomation syndrome is addressed. Special attention is paid to available antivenoms, their indications for use, and their side effects.

Coexistence of Rhabdomyolysis, Myocarditis and Arrhythmia after Spider Bite: A Case Report.

BACKGROUND: Rhabdomyolysis after spider bite has been reported in a small number of patients, and myocarditis in even fewer. However, arrhythmia associated with latrodectism in children has not been described in the literature to date. CASE SUMMARY: A girl presented approximately 4.5 h after being bitten on the left ankle by a black spider. Two unifocal premature ventricular contractions (PVCs) were observed on the electrocardiogram. In laboratory tests, creatine kinase was elevated. On day 2, levels of troponin, pro-brain and natriuretic peptide were elevated. Electrocardiogram revealed inverted and biphasic T waves. Echocardiography revealed mild left ventricular dilation, mitral and aortic valve regurgitation. Holter electrocardiogram showed PVCs. Her laboratory and echocardiography findings completely normalized after discharge, and no arrhythmia was observed on the Holter electrocardiogram during outpatient follow-up. CONCLUSION: Although spider bites are uncommon, they can cause serious systemic effects. These patients should be evaluated for arrhythmia, rhabdomyolysis and myocarditis.

Rarely, spider bites can cause serious systemic effects, severe morbidity and death. In a small number of patients, spider envenomation causes rhabdomyolysis and myocarditis. In the present case, the elevated troponin and pro-brain natriuretic peptide levels and electrocardiogram/echocardiography findings were consistent with myocarditis, and an increase in creatinine kinase level indicated rhabdomyolysis. In addition, the electrocardiogram and Holter electrocardiogram revealed unifocal premature ventricular contraction. To our knowledge, arrhythmia due to Latrodectus spider bite has not been described in children to date. In addition, this case demonstrates the coexistence of two serious systemic effects, rhabdomyolysis and myocarditis, with full recovery after appropriate treatment.

An RBF neural network based on improved black widow optimization algorithm for classification and regression problems.

Introduction: Regression and classification are two of the most fundamental and significant areas of machine learning. Methods: In this paper, a radial basis function neural network (RBFNN) based on an improved black widow optimization algorithm (IBWO) has been developed, which is called the IBWO-RBF model. In order to enhance the generalization ability of the IBWO-RBF neural network, the algorithm is designed with nonlinear time-varying inertia weight. Discussion: Several classification and regression problems are utilized to verify the performance of the IBWO-RBF model. In the first stage, the proposed model is applied to UCI dataset classification, nonlinear function approximation, and nonlinear system identification; in the second stage, the model solves the practical problem of power load prediction. Results: Compared with other existing models, the experiments show that the proposed IBWO-RBF model achieves both accuracy and parsimony in various classification and regression problems.

Attribute-Based Adaptive Homomorphic Encryption for Big Data Security.

There is a drastic increase in Internet usage across the globe, thanks to mobile phone penetration. This extreme Internet usage generates huge volumes of data, in other terms, big data. Security and privacy are the main issues to be considered in big data management. Hence, in this article, Attribute-based Adaptive Homomorphic Encryption (AAHE) is developed to enhance the security of big data. In the proposed methodology, Oppositional Based Black Widow Optimization (OBWO) is introduced to select the optimal key parameters by following the AAHE method. By considering oppositional function, Black Widow Optimization (BWO) convergence analysis was enhanced. The proposed methodology has different processes, namely, process setup, encryption, and decryption processes. The researcher evaluated the proposed methodology with non-abelian rings and the homomorphism process in ciphertext format. Further, it is also utilized in improving one-way security related to the conjugacy examination issue. Afterward, homomorphic encryption is developed to secure the big data. The study considered two types of big data such as adult datasets and anonymous Microsoft web datasets to validate the proposed methodology. With the help of performance metrics such as encryption time, decryption time, key size, processing time, downloading, and uploading time, the proposed method was evaluated and compared against conventional cryptography techniques such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). Further, the key generation process was also compared against conventional methods such as BWO, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results established that the proposed method is supreme than the compared methods and can be applied in real time in near future.

Fuzzy segmentation and black widow-based optimal SVM for skin disease classification.

The skin, which has seven layers, is the main human organ and external barrier. According to the World Health Organization (WHO), skin cancer is the fourth leading cause of non-fatal disease risk. In medicinal fields, skin disease classification is a major challenging issue due to inaccurate outputs, overfitting, larger computational cost, and so on. We presented a novel approach of support vector machine-based black widow optimization (SVM-BWO) for skin disease classification. Five different kinds of skin disease images are taken such as psoriasis, paederus, herpes, melanoma, and benign with healthy images which are chosen for this work. The pre-processing step is handled to remove the noises from the original input images. Thereafter, the novel fuzzy set segmentation algorithm subsequently segments the skin lesion region. From this, the color, gray-level co-occurrence matrix texture, and shape features are extracted for further process. Skin disease is classified with the usage of the SVM-BWO algorithm. The implementation works are handled in MATLAB-2018a, thereby the dataset images were collected from ISIC-2018 datasets. Experimentally, various kinds of performance analyses with state-of-the-art techniques are performed. Anyway, the proposed methodology outperforms better classification accuracy of 92% than other methods. Workflow diagram of the proposed methodology.