MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection.

Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a wide range of device malfunctions, overloads, and network intrusions. As a result of this, the network's normal operation and services will be disrupted. The paper proposes a new multi-variant time series-based encoder-decoder system for dealing with anomalies in time series data with multiple variables. As a result, to update network weights via backpropagation, a radical loss function is defined. Anomaly scores are used to evaluate performance. The anomaly score, according to the findings, is more stable and traceable, with fewer false positives and negatives. The proposed system's efficiency is compared to three existing approaches: Multiscaling Convolutional Recurrent Encoder-Decoder, Autoregressive Moving Average, and Long Short Term Medium-Encoder-Decoder. The results show that the proposed technique has the highest precision of 1 for a noise level of 0.2. Thus, it demonstrates greater precision for noise factors of 0.25, 0.3, 0.35, and 0.4, and its effectiveness.

Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques.

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry's clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS's performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.