A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS: In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks.

The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

Health Index Estimation of Wind Power Plant Using Neurofuzzy Modeling.

According to the Tamil Nadu Energy Development Agency (TEDA) in the 2019-20 academic year, the wind power plant produces 23% of the biomass power supply in the Indian electrical commodities. To maintain the power withstanding capability needed for future electrical commodities, a yearly power shutdown program is implemented. An additional wind power plant unit will be erected and create more electricity, thereby balancing India's commercial electricity needs. Even in a nonstationary working environment, continuous monitoring and analyzing the efficiency of wind turbines is a more difficult task. Consequently, in this paper, a health index calculation for wind power plants is proposed utilizing neurofuzzy (NF) modeling. Wind generator efficiency can be measured mathematically by recording three crucial primitivistic such as observed rotation speed, generation wound temperature, and gearbox heat. Fuzzy rules are used to design the parameters of the neural network (NN), and the accumulated signal is compared using the nonlinear extrapolation approach to determine the wind generator's behavior and evaluate the hazards. During the experimental study, two windows of 24 hours and 60 hours are used, where the deviation signal required for the hazard induction is investigated. The proposed approach can accurately calculate the wind generator's health state. As a result of an improved health operating and management (HOM) system, the amount of power generated by industrials and domestic appliances has increased dramatically.

Deep Learning in Healthcare System for Quality of Service.

Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.

Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System.

This paper introduces the application and classification of an adaptive filtering algorithm in the image enhancement algorithm. And the filtering noise reduction impact is compared using MATLAB software for programming, image processing, LMS algorithm, RLS algorithm, histogram equalisation algorithm, and Wiener filtering method filtering noise reduction effect. To optimize the intelligent graphic image interaction system, the proposed nonlinear adaptive algorithm of intelligent graphic image interaction system research is based on the digital filter and adaptive filtering algorithm for simulation experiment. The experimental results of several noise index data filtering algorithms show that the fuzzy coefficient k of LMS index is 0.86, RLS index is 0.91, the histogram equalization index is 0.53, and the Wiener filtering index is 0.62. LMS index of quality index Q is 0.90, RLS index is 0.95, histogram equalization index is 0.58, Wiener filtering index is 0.65. According to the above results, comparing LMS with the RLS method and according to SNR, k, and Q values in the simulation results in the process of processing, it is found that the convergence speed of the RLS algorithm is obviously better than that of the LMS algorithm, and the stability is also good. Additionally, the differential imaging data can provide a strong reference for the clinical diagnosis and qualitative differentiation of TBP and CP, and MSCT is worthy of extensive application in the clinical diagnosis of peritonitis. The processing effect of the image with high similarity to the original image is greatly improved compared with the histogram equalization and Wiener filtering methods used in the simulation.