Internet of Things brings Revolution in eHealth: Achievements and Challenges

The medical field has benefited greatly from the technological revolution around our world, as well as the introduction of artificial intelligence (AI) and the Internet of Things (IoT). IoT aims to make life easier and more convenient by bridging the various gaps in connecting various devices that people employ. A wide range of applications and technologies, including wearable device development, advanced care services, personalized care packages, and remote patient monitoring, benefit healthcare professionals and patients. These technologies gave rise to new terms such as the Internet of Medical Things (IoMT), the Internet of Health Things (IoHT), e-Health, and telemedicine. With the advent of technology and the availability of various connected devices, smart healthcare, which has grown in popularity in recent years, has been positively redefined. Through the selection of literature reviews, we systematically investigate how the adoption (and integration) of IoT technologies in healthcare is changing the way traditional services and products are delivered. This paper outlines (i) selected IoT technologies and paradigms related to health care, as well as, (ii) various implementation scenarios for IoT-based models. It also discusses (iii) the various advantages of these applications and finally, (iv) a summary of lessons learned and recommendations for future applications

Deep Learning-based Gated Recurrent Unit Approach to Stock Market Forecasting: An Analysis of Intel\u27s Stock Data

The stock price index prediction is a very challenging task that\u27s because the market has a very complicated nonlinear movement system. This fluctuation is influenced by many different factors. Multiple examples demonstrate the suitability of Machine Learning (ML) models like Neural Network algorithms (NN) and Long Short-Term Memory (LSTM) for such time series predictions, as well as how frequently they produce satisfactory outcomes. However, relatively few studies have employed robust feature engineering sequence models to forecast future prices. In this paper, we propose a cutting-edge stock price prediction model based on a Deep Learning (DL) technique. We chose the stock data for Intel, the firm with one of the quickest growths in the past ten years. The experimental results demonstrate that, for predicting this particular stock time series, our suggested model outperforms the current Gated Recurrent Unit (GRU) model. Our prediction approach reduces inaccuracy by taking into account the random nature of data on a big scale

Credit Card Fraud Detection Using Logistic Regression and Synthetic Minority Oversampling Technique (SMOTE) Approach

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today\u27s culture are credit card scams. This kind of fraud typically happens when someone uses someone else\u27s credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models\u27 reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft