Search

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hosseinzadeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues. [ABSTRACT FROM AUTHOR]

Typing is a time-consuming task and predictive text is proposed as a solution. Recently, Generative Pre-trained Transformers (GPT) have employed autoregressive deep learning to tackle text prediction. However, they face costly retraining, especially for low-resource languages (such as Persian) or domains, and lack controllability. Text augmentation with prompting methods and fine-tuning GPT models on templated data using conditional elements (labels or keywords) aims to address these problems, enhancing controllable text generation in low-resource scenarios. These methods involve discovering and mapping conditional elements to training texts, which is unsuitable for typing assistants. Meanwhile, they do hold inappropriate pre-defined elements for wide use. This paper introduces Conditional GPT-2-Persian (CGPT-2-Persian), which utilizes the initial word of each sentence as the associated conditional element to address practical challenges, extracting labels, and handling in-domain unseen data posed by the mentioned methods. This method outperforms Persian text generation methods in terms of BLEU and ROUGE scores, achieving 87.39% and 14.29%, respectively, after fine-tuning for ten epochs. This study can be efficient for other low-resource languages or domains. [ABSTRACT FROM AUTHOR]