A Machine Learning-based Approach for Intrusion Detection and Prevention in Computer Networks

The potential of cyberattacks and network penetration has increased due to modern enterprises' increasing reliance on computer networks. Such attacks are detected and prevented by intrusion detection and prevention systems (IDPS), although conventional rule-based solutions have difficulties identifying unidentified attacks. Due to its capacity to learn from data and spot patterns of assault that conventional methods could miss, machine learning (ML) techniques have been gaining prominence in IDPS. This article provides a thorough analysis of the several ML methods utilized in IDPS, including supervised, unsupervised, and hybrid techniques. Also, a hybrid ML-based IDPS that combines the advantages of several methodologies for better performance is proposed. Furthermore, covered are the difficulties with ML-based IDPS and potential solutions. It is demonstrated how ML-based IDPS may be applied in real-world situations, emphasizing the advantages of applying ML to intrusion detection and prevention. In conclusion, this study offers insights into the most recent methods for ML-based IDPS and their potential to enhance network security

Constraining axionlike particles with invisible neutrino decay using the IceCube observations of NGC 1068

In the beyond Standard Model (BSM) scenarios, the possibility of neutrinos decaying into a lighter state is one of the prime quests for the new-generation neutrino experiments. The observation of high-energy astrophysical neutrinos by IceCube opens up a new avenue for studying neutrino decay. In this work, we investigate a novel scenario of invisible neutrino decay to axionlike particles (ALPs). These ALPs propagate unattenuated and reconvert into gamma rays in the magnetic field of the Milky Way. This is complementary and independent of the previously done studies where gamma rays produced at the source are used to investigate the ALP hypothesis. We exploit the Fermi-LAT and IceCube observations of NGC 1068 to set constraints on the ALP parameters. Being a steady source of neutrinos, it offers a better prospect over transient sources. We obtain 95% confidence level (CL) upper limits on the photon-ALP coupling constant $g_{a\gamma}\lesssim 1.37 \times 10^{-11}$ GeV$^{-1}$ for ALP masses $m_{a} \leq 2 \times 10^{-9}$ eV. Our results are comparable to previous upper limits obtained using the GeV to sub-PeV gamma-ray observations. Moreover, we estimate the contribution from NGC 1068-like sources to diffuse gamma-ray flux at GeV energies under the ALP scenario.Comment: 11 pages, 5 figures; matches with the published versio

Forecast of Stock Market using Machine Learning Strategies

Accurately predicting the stock market is one of the things that investors are most interested in since it may help them make money in the economy. Given that such markets are significantly influenced by volatility and news, it is difficult to predict stock prices, which are solely dependent on market timing. Owing to this difficulty and volatility, it is vital to evaluate stock forecasting using historical data as well as external variables such as investor behaviour, social media, and financial news. Thus, this study recommends using regression and machine learning algorithms to estimate the price of equities on upcoming days based on investor sentiment. The experiment is conducted on Yahoo Finances and combines Twitter repository data on investor sentiment. In the subsequent step, the concept of sentiment analysis is applied to the monitoring of Twitter user tweets. The tweets are then sorted into positive and negative groups based on the sentiment score. In addition, machine learning algorithms are used to forecast Yahoo Finance stock values. To solve this issue, we propose reducing the complexity of time sequence models by employing regression approaches that integrate a hybridized concept of sentiment analysis and machine learning algorithms, which may result in higher accuracy. The testing results validate the best linear regression prediction accuracy and demonstrate an overall system performance enhancement

Probing photon-ALP oscillations from the MAGIC observations of FSRQ QSO B1420+326

At the beginning of the year 2020, MAGIC reported a very-high-energy (VHE) flaring activity from the FSRQ QSO B1420+326. It is now the fourth known most distant blazar (z=0.682) with an observed VHE gamma-ray emission. In this work, we investigate the effect of photon-axion-like particle (ALP) oscillations in the gamma-ray spectra measured by Fermi-LAT and MAGIC around the flaring state. We set 95% confidence level (C.L.) upper limit on the ALP parameters and obtain a constraint on the photon-ALP coupling constant $g_{a\gamma} < 2\times10^{-11}$ GeV$^{-1}$ for ALP masses $m_{a} \sim 10^{-10}-10^{-9}$ eV. Assuming the hadronic origin of VHE photons, we also estimate the expected neutrino flux from this source and the contribution to diffuse neutrino flux from QSO B1420+326-like FSRQs at sub-PeV energies. Furthermore, we study the implications of photon-ALP oscillations on the counterpart $\gamma$-rays of the sub-PeV neutrinos. Finally, we investigate a viable scenario of invisible neutrino decay to ALPs on the gamma-ray spectra and diffuse $\gamma$-ray flux at sub-PeV energies. Interestingly, we find that for the choice of neutrino lifetime $\tau_{2}/m_{2} = 10^3$ s eV$^{-1}$, the $\gamma$-ray flux has a good observational sensitivity towards LHAASO-KM2A.Comment: 11 pages, 7 figures; comments and feedback are welcom

A COHORT STUDY COMPARING THE SHORT-TERM OUTCOME OF NEWBORN INFANTS: SPINAL VERSUS GENERAL ANAESTHESIA IN ELECTIVE CESAREAN SECTION.

Background: During C-sections, general anesthesia is required to guarantee the safety of the fetus and the mother. In this retrospective cohort investigation, average variations in hematocrit and predicted loss of blood, newborn Apgar evaluation at one and five minutes, and postoperative hemodynamic parameters (prior- and following surgery systolic blood pressure, heart rate) were used to compare maternal and fetal results among general and spinal sedation for C-sections. The study aims to compare maternal and fetal outcomes between spinal anesthesia and general anesthesia in elective cesarean sections. Methods: A retrospective study was performed on information collected from electronic health records of 227 pregnancies with one child between X to Y; 200 instances were given to the spinal category (n = 100) or general category (n = 100), and 27 cases were excluded. Results: The overall organization's afterward hemodynamic results (SBP: 136 ± 16.5 vs. 120 ± 12.5 mmHg, heart rate: 93.0 ± 17 vs. 71.0 ± 12.5 beats per min, P &lt; 0.001) were significantly greater than those of the spinal category. Furthermore, a statistically significant distinction was observed (P &lt; 0.001) between the prior and afterward hematocrit in the overall category compared to the spinal category (4.8 ± 3.5% vs. 2.3 ± 4.0%, each). In the overall category, compared to the spinal category, there was a greater percentage of newborns with 5-min Apgar scores &lt; 7 (6/141 [4.3%] vs. 0/146 [0%], accordingly, P = 0.012). Conclusion: Compared to the spinal category during cesarean sections, the general category is linked to greater maternal loss of blood and a higher percentage of infants with 5-minute Apgar evaluation &lt; 7. Recommendations: Based on the study's findings, it is recommended to prefer spinal anesthesia over general anesthesia for elective cesarean sections to minimize maternal blood loss and improve neonatal Apgar scores​

Wind Speed Estimation Based Sensorless Output Control for A Wind Turbine Driving A DFIG

A specific design of the proposed control algorithm for a wind turbine equipped with a doubly fed induction generator (DFIG) is presented. The aerodynamic characteristics of the wind turbine are approximated by a Gaussian radial basis function network based nonlinear input-output mapping. Based on this nonlinear mapping, the wind speed is estimated from the measured generator electrical output power while taking into account the power losses in the WTG and the dynamics of the WTG shaft system. The new control methodology means the fuzzy logic controller has been developed and evaluated in detail. Finally, the proposed method is applied to the wind generation system The estimated wind speed is then used to determine the optimal DFIG rotor speed command for maximum wind power extraction. The DFIG speed controller is suitably designed to effectively damp the low-frequency torsional oscillations. The resulting WTG system delivers maximum electrical power tothe grid with high efficiency and high reliability without mechanical anemometers

Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network

/n This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector. Using transfer learning, deep features are extracted from a modified DCNN, whose training is performed on 69% of randomly selected images of database from both categories. Features of Grey Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are merged to form texture features. Mean and variance of four parameters (contrast, correlation, homogeneity and entropy) of GLCM are computed in four angular directions, at ten distances. Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector

To Identify Hypertensive Attack and Alert Healthcare Framework using IOT-FOG

This paper presents a wearable health sensor network system for Internet of Things (IoT) connected safety and health applications. Safety and health of icu patient are important in hospital workplace; therefore, an IoT network system which can monitor all health parameters and update through wireless. The proposed network system incorporates multiple wearable sensors to monitor environmental and physiological parameters. The wearable sensors on different subjects can communicate with each other and transmit the data to a gateway via IoT platform medical signal sensing network. In the proposed system having heart rate, temperature, vibration sensors all integrated to the parallel processing microprocessor. Health parameters re measured by sensors and give the RPI PICO module. This module analyses the data aand monitor in LCD, post the same in internet of things-based server. We continuously monitor, if any changes found like low heart rate, high heart rate, high temperature, patient movement iot alerts the authorized person regarding health A smart IoT gateway is implemented to provide data processing, local web server and cloud connection. After the gateway receives the data from wearable sensors, it will forward the data to an IoT cloud for further data storage, processing, and visualizatio

Pharmacoeconomic analysis of drugs used for peptic ulcer in India

Background: Acid peptic disorders are common medical problems in daily clinical practice leading to a significant economic burden on healthcare expenses. Due to lack of information on comparative drug prices and quality, it becomes difficult for physicians to prescribe the most economical treatment. So the present study was planned to analyse the price variations of various anti-ulcer drugs available in India.Methods: The cost of a particular anti-ulcer drug being manufactured by different companies, in the same dose and dosage forms, was obtained from latest issue of ‘‘current index of medical specialties’’ January to April, 2016. The difference between the maximum and minimum prices of same drug was analysed and percentage variation in the prices was calculated.Results: Overall, the prices of a total of 12 anti-ulcer drugs belonging to four different categories available in 38 different formulations were analysed.  Among the proton pump inhibitors, pantoprazole (40 mg; EC tablet) showed the maximum price variation of 500.75%. With regard to H2 blockers, ranitidine (50 mg; injection) showed the maximum price variation of 989.92%. The maximum price variation among various formulations of ulcer protective was seen with sucralfate (1000 mg; tablet) of 166.00% while misoprostol (200µg; tablet) was the only drug present in prostaglandin analogues and it showed a price variability of 14.33%.Conclusions: The average percentage variations of different brands of the same anti-ulcer drugs in same dose and dosage form manufactured in India were very wide. The government and drug manufacturing companies must direct their efforts in reducing the cost of anti-ulcer drugs and thereby minimizing the economic burden on the patients