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The aim of computer vision techniquesand deep learning in the era of digitalization is to derive valuable insights from them and generate novel understanding. This makes it possible to employ imaging to quickly diagnose and treat a variety of diseases. In the field of dermatology, deep neural networks are utilized to differentiate between images of melanoma and non-melanoma skin lesions. In this paper, we have emphasised two important aspects of melanoma detection research. The accuracy of classifiers is the first thing to take into account, even with very little modifications to the dataset's characteristics there will be a lot of difference in accuracy. We investigated transfer learning issues in this case. We propose that continual training-test iterations are necessary to create reliable prediction models based on the results of the initial study.The second argument is the need for a system with a flexible design that can accommodate changes to training datasets.Our proposal for creating and implementing a melanoma detection service that utilizes clinical and thermoscopic images involves the development and implementing a hybrid architecture that fuses fog, edge and cloud computing. In addition, this design should aim to decrease the duration of the ongoing retraining process, which is necessary to accommodate the large volume of data that requires evaluation. This notion has been reinforced by experiments using a single computer and a variety of distribution techniques, which show how a dispersed strategy ensures output attainment in a noticeably more sufficient amount of time.

Inthispaper,weconductanempiricalstudytocompare the performance of two popular approaches for multi-label text classification, which is a challenging task in naturallanguageprocessingthatrequirespredictingmultiplelabelsforagiventext:One-vs-RestandVotingclassifiers.Weevaluatetheseclassifiersonadatasetoftoxiccommentsandmeasuretheir performance using accuracy and hamming loss evaluationmetrics.OurexperimentalresultsshowthattheOne-vs-RestclassifierwithXGBoutperformstheVotingclassifierandachievesanaccuracyof91.7%.Thestudy’sresultscanbeusedasabenchmarkforfutureresearchinthisarea,andtheinsightsgained can be used to improve the accuracy and robustness ofmulti-label text classification models. Furthermore, our findingssuggest that the One-vs-Rest classifier with XGB is a promisingapproachformulti-labeltextclassificationtasks,whichcanprovidebetterresultsthanotherpopularclassifiers

Bitcoin's decentralized nature has made it a popular mode of payment for buyers and sellers, but its highly volatile nature poses a challenge for investors. This study aims to predict future Bitcoin prices using a combination of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. The non-stationary nature of Bitcoin prices is addressed using RNN, which is particularly useful for analyzing sequential data.The dataset used in this research is sourced from the Kraken exchange and includes various factors that are believed to influence Bitcoin prices, such as transaction volume, hash rate, and Google search trends. The data is preprocessed and cleaned to ensure accuracy, and then fed into the RNN and LSTM models for training and testing.The study's use of RNN and LSTM algorithms demonstrates the effectiveness of these methods in predicting Bitcoin prices, particularly in the context of sequential data. The results of the study, provide insights into potential future trends in Bitcoin prices and identify key indicators that significantly influence Bitcoin prices. The findings of this research have important implications for investors and traders looking to make informed decisions in the cryptocurrency market, as well as for researchers seeking to improve our understanding of Bitcoin's price dynamics. By predicting future prices, the study provides insights that can mitigate the risks associated with Bitcoin's volatility, making it a more viable investment option.