Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise (...) to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions. (shrink)

Speech is a primary means of human communication and one of the most basic features of human conduct. Voice is an important part of its subsystems. A speech disorder is a condition that affects the ability of a person to speak normally, which occasionally results in voice impairment with psychological and emotional consequences. Early detection of voice problems is a crucial factor. Computer-based procedures are less costly and easier to administer for such purposes than traditional methods. This study highlights the (...) following issues: recent studies, methods of voice pathology detection, machine learning and deep learning (DL) methods used in data classification, main datasets utilized, and the role of Internet of things (IoT) systems employed in voice pathology diagnosis. Moreover, this study presents different applications, open challenges, and recommendations for future directions of IoT systems and artificial intelligence (AI) approaches in the voice pathology diagnosis. Finally, this study highlights some limitations of voice pathology datasets in comparison with the role of IoT in the healthcare sector, which shows the urgent need to provide efficient approaches and easy and ideal medical diagnostic procedures and treatments of disease identification for doctors and patients. This review covered voice pathology taxonomy, detection techniques, open challenges, limitations, and recommendations for future directions to provide a clear background for doctors and patients. Standard databases, including the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and the Arabic Voice Pathology Database, were used in most articles reviewed in this article. The classes, features, and main purpose for voice pathology identification are also highlighted. This study focuses on the extraction of voice pathology features, especially speech analysis, extends feature vectors comprising static and dynamic features, and converts these extended feature vectors into solid vectors before passing them to the recognizer. (shrink)

Liquidity risk arises from the inability to unwind or hedge trading positions at the prevailing market prices. The risk of liquidity is a wide and complex topic as it depends on several factors and causes. While much has been written on the subject, there exists no clear-cut mathematical description of the phenomena and typical market risk modeling methods fail to identify the effect of illiquidity risk. In this paper, we do not propose a definitive one either, but we attempt to (...) derive novel mathematical algorithms for the dynamic modeling of trading volumes during the closeout period from the perspective of multiple-asset portfolio, as well as for financial entities with different subsidiary firms and multiple agents. The robust modeling techniques are based on the application of initial-value-problem differential equations technique for portfolio selection and risk management purposes. This paper provides some crucial parameters for the assessment of the trading volumes of multiple-asset portfolio during the closeout period, where the mathematical proofs for each theorem and corollary are provided. Based on the new developed econophysics theory, this paper presents for the first time a closed-form solution for key parameters for the estimation of trading volumes and liquidity risk, such as the unwinding constant, half-life, and mean lifetime and discusses how these novel parameters can be estimated and incorporated into the proposed techniques. The developed modeling algorithms are appealing in terms of theory and are promising for practical econophysics applications, particularly in developing dynamic and robust portfolio management algorithms in light of the 2007–2009 global financial crunch. In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things data analytics, expert systems in finance, FinTech, and within big data ecosystems. (shrink)

The article focuses on Hryhorii Skovoroda’s philosophical dialogue dedicated to the nature of human and happiness as a bright example of a harmonious fusion of philosophical ideas and individual style. A comparative analysis based on a hermeneutic approach helped to assess the equivalency in representing the lexical, syntactical, and emotional levels of the reconstructed Ukrainian version of Skovoroda’s dialogue via English translation, and thus contribute to clarifying the reliable strategies of translating a chronologically remote text of philosophical discourse. The research (...) stresses the importance for a translator of philosophical works of Hryhorii Skovoroda to thoroughly consider the contextual meaning, inseparability of the concepts and style, and unique syntactic and emotive patterns of the source text conflating explicit and implicit semantics. In addition to deep understanding by translators of key philosophical terms, charged with contextual connotations, an equivalent translation of philosophic texts also demands sufficient knowledge of the related historical, cultural, and linguistic background to ensure a comprehensive perception of the intended senses and vectors of the mental activity by a reader. (shrink)

Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is (...) for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest. (shrink)

Given the wide-reaching and detrimental impact of COVID-19, its strain on healthcare resources, and the urgent need for—sometimes forced—public health interventions, thorough examination of the ethical issues brought to light by the pandemic is especially warranted. This paper aims to identify some of the complex moral dilemmas faced by senior physicians at a major medical centre in Saudi Arabia, in an effort to gain a better understanding of how they navigated ethical uncertainty during a time of crisis. This qualitative study (...) uses a semistructured interview approach and reports the findings of 16 interviews. The study finds that participants were motivated by a profession-based moral obligation to provide care during the toughest and most uncertain times of the pandemic. Although participants described significant moral dilemmas during their practice, very few identified challenges as ethical in nature, and in turn, none sought formal ethics support. Rather, participants took on the burden of resolving ethical challenges themselves—whenever possible—rationalising oft fraught decisions by likening their experiences to wartime action or by minimising attention to the moral. In capturing these accounts, this paper ultimately contemplates what moral lessons can, and must be, learnt from this experience. (shrink)