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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.

Summary: Internet of Things (IoT) is an intelligent technology and service that mutually communicates information between human and devices or between Internet‐based devices. In IoT security, the authentication should be distributed and lightweight. Token generation should be based on the trust worthiness of the devices, and it needs to ensure fast authentication and authorization. In this paper, we propose an Energy Efficient Distributed Lightweight Authentication and Encryption (EEDLAE) technique for IoT security. In our proposed technique, the receiver generates the token for each sender. The token expiration time is determined based on the trust value of each sender, and sleep period of the receiver radio is determined based on its remaining energy. For encryption, Counter with Cipher Block Chaining‐Message Authentication Code (CCM) is applied. Experimental results show that EEDLAE technique can hold out against affected packets, higher resilience against node capture, increased throughput, and residual energy, compared with the existing technique. [ABSTRACT FROM AUTHOR]

The dimension of worry for drivers will have a more prominent effect while driving. Driver stress may influence driver execution and makes numerous mishaps. In some cases numerous blameless lives are lost due to driver feeling of anxiety. Stress is something we can't ready to physically observe and recognize, hence extraordinary kinds of sensors like heartbeat, ECG, understudy expansion, circulatory strain, breath rate, skin reaction are utilized to foresee feeling of anxiety of the driver and driver conduct while driving. Driver conduct parameters like ECG, Eye flickering, speed, controlling point, and turn flag can be checked while driving. These parameters may be useful to eyewitness driver's diversions also. In this task, to defeat the test we have proposed genuine information gathering, IoT based sharing and information examination. To acquire driver stress we are intending to coordinate heartbeat and eye flicker sensors to anticipate the feeling of anxiety and languor of the driver amid driving. Progressively these sensors would be installed in the driver glass. The driver information are gathered from the sensors transmitted to the microcontroller and utilizing RS232 link the genuine and dynamic driver information are transmitted to the framework. The ongoing information from the COM port are gotten utilizing Net beans and spared in SQL database. The ongoing information can be checked by vehicle administration focuses, transport proprietors and adjacent vehicle boarding point. The driver information are put away in open cloud server in particular CloudMe. [ABSTRACT FROM AUTHOR]