A comparison was made of two eddy dissipation rate (EDR) estimates based on flight data recorded by commercial flights. The EDR estimates from real-time data using the National Center for Atmospheric Research (NCAR) Algorithm were compared with the EDR estimates derived using the Netherlands Aerospace Centre (NLR) Algorithm using quick assess recorder (QAR) data. The estimates were found to be in good agreement in general, although subtle differences were found. The agreement between the two algorithms was better when the flight was above 10,000 ft. The EDR estimates from the two algorithms were also compared with the vertical acceleration experienced by the aircraft. Both EDR estimates showed good correlation with the vertical acceleration and would effectively capture the turbulence subjectively experienced by pilots.
The age of easy oil is ending, the industry started drilling in remote unconventional conditions. To help produce safer, faster, and most effective operations, the utilization of artificial intelligence and machine learning (AI/ML) has become essential. Unfortunately, due to the harsh environments of drilling and the data-transmission setup, a significant amount of the real-time data could defect. The quality and effectiveness of AI/ML models are directly related to the quality of the input data; only if the input data are good, the AI/ML generated analytical and prediction models will be good. Improving the real-time data is therefore critical to the drilling industry. The objective of this paper is to propose an automated approach using eight statistical data-quality improvement algorithms on real-time drilling data. These techniques are Kalman filtering, moving average, kernel regression, median filter, exponential smoothing, lowess, wavelet filtering, and polynomial. A dataset of +150,000 rows is fed into the algorithms, and their customizable parameters are calibrated to achieve the best improvement result. An evaluation methodology is developed based on real-time drilling data characteristics to analyze the strengths and weaknesses of each algorithm were highlighted. Based on the evaluation criteria, the best results were achieved using the exponential smoothing, median filter, and moving average. Exponential smoothing and median filter techniques improved the quality of data by removing most of the invalid data points, the moving average removed more invalid data-points but trimmed the data range.
An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and prone to obsolescence. Further, the need to protect private data and conceal sensitive data features has limited data sharing, thus encouraging the use of synthetic data for training predictive and intrusion detection models. However, synthetic data can be unrealistic and potentially bias. On the other hand, real-time data are realistic and current; however, it is inherently imbalanced due to the uneven distribution of anomalous and non-anomalous examples. In general, non-anomalous or normal examples are more frequent than anomalous or attack examples, thus leading to skewed distribution. While imbalanced data are commonly predominant in intrusion detection applications, it can lead to inaccurate predictions and degraded performance. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. This paper investigates imbalanced learning and adversarial learning for training AN-Intel-IDS using a qualitative study. It surveys and synthesizes generative-based data augmentation techniques for addressing the uneven data distribution and generative-based adversarial techniques for generating synthetic yet realistic data in an adversarial setting using rapid review, structured reporting, and subgroup analysis.
Semi-stream join is an emerging research problem in the domain of near-real-time data warehousing. A semi-stream join is basically a join between a fast stream (S) and a slow disk-based relation (R). In the modern era of technology, huge amounts of data are being generated swiftly on a daily basis which needs to be instantly analyzed for making successful business decisions. Keeping this in mind, a famous algorithm called CACHEJOIN (Cache Join) was proposed. The limitation of the CACHEJOIN algorithm is that it does not deal with the frequently changing trends in a stream data efficiently. To overcome this limitation, in this paper we propose a TinyLFU-CACHEJOIN algorithm, a modified version of the original CACHEJOIN algorithm, which is designed to enhance the performance of a CACHEJOIN algorithm. TinyLFU-CACHEJOIN employs an intelligent strategy which keeps only those records of $R$ in the cache that have a high hit rate in S. This mechanism of TinyLFU-CACHEJOIN allows it to deal with the sudden and abrupt trend changes in S. We developed a cost model for our TinyLFU-CACHEJOIN algorithm and proved it empirically. We also assessed the performance of our proposed TinyLFU-CACHEJOIN algorithm with the existing CACHEJOIN algorithm on a skewed synthetic dataset. The experiments proved that TinyLFU-CACHEJOIN algorithm significantly outperforms the CACHEJOIN algorithm.
Since 2019, the SARS-CoV-2 pandemic has caused a huge chaos throughout the world and the major threat has been possessed by the immune-compromised individuals involving the cancer patients; their weakened immune response makes them vulnerable and susceptible to the virus. The oncologists as well as their patients are facing many problems for their treatment sessions as they need to postpone their surgery, chemotherapy, or radiotherapy. The approach that could be adopted especially for the cancer patients is the amalgamation of immunotherapy and nanotherapy which can reduce the burden on the healthcare at this peak time of the infection. There is also a need to predict or analyze the data of cancer patients who are at a severe risk of being exposed to an infection in order to reduce the mortality rate. The use of artificial intelligence (AI) could be incorporated where the real time data will be available to the physicians according to the different patient’s clinical characteristics and their past treatments. With this data, it will become easier for them to modify or replace the treatment to increase the efficacy against the infection. The combination of an immunotherapy and nanotherapy will be targeted to treat the cancer patients diagnosed with SARS-CoV-2 and the AI will act as icing on the cake to monitor, predict and analyze the data of the patients to improve the treatment regime for the most vulnerable patients.