Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

Aims: The aim of this study was to evaluate the value and accuracy of longitudinal strain in detection of coronary artery disease compared to coronary angiography. Results: The left ventricular longitudinal strain-speckle tracking showed evidence of stenosis of left anterior descending artery, circumflex artery and right coronary artery in (86.1%), (76.4%), and (84.7%) respectively. For the stenosis in left anterior descending artery, the current study showed that the longitudinal strain was a good predictor for presence of significant stenosis with a sensitivity of (93.8%), specificity (75%) and accuracy (91.7%) compared with coronary angiography. For the stenosis in right coronary artery, the left ventricular longitudinal strain had

Intrusion detection system is an imperative role in increasing security and decreasing the harm of the computer security system and information system when using of network. It observes different events in a network or system to decide occurring an intrusion or not and it is used to make strategic decision, security purposes and analyzing directions. This paper describes host based intrusion detection system architecture for DDoS attack, which intelligently detects the intrusion periodically and dynamically by evaluating the intruder group respective to the present node with its neighbors. We analyze a dependable dataset named CICIDS 2017 that contains benign and DDoS attack network flows, which meets certifiable criteria and is ope

The control of water represents the safe key for fair and optimal use to protect water resources due to human activities, including untreated wastewater, which is considered a carrier of a large number of antibiotic-resistant bacterial species. This study aimed to investigate the prevalence of antibiotic-resistance to E. coli in Tigris River by the presence of resistance genes for aminoglycoside(qepA( ,quinolone (gyrA), and sulfa drugs( dfr1 ,dfr17) due to the frequent use of antibiotics and their release into wastewater of hospitals. Samples were collected from three sites on Tigris River: S1( station wastewater in Adhamiya), S2 (station wastewater in Baghdad Medical city hospital), S3 (station wastew

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

BCl3 is toxic gas and its detection is of great importance. Thus, here, B3LYP, M06-2X, and B97D density functionals are utilized for probing the effect of decorating Zn, Cd, and Au on the sensing performance of an AlP nano-sheet (AlPNS) in detecting the BCl3. We predict that the interaction of pure AlPNS with BCl3 is physisorption, and the sensing response (SR) of AlPNS is approximately 9.2. The adsorption energy of BCl3 changes from −4.1 to −18.8, −19.1, and −19.5 kcal/mol by decorating the Zn, Cd, and Au metals into the AlPNS surface, respectively. Also, the corresponding SR meaningfully rises to 40.4, 59.0, and 80.9, indicating that by increasing the atomic number of metals, the sensitivity of metal decorated AlPNS (metal@AlPNS)

Five Saccharomyces cerevisiae isolated from the ability of chitinase production from the isolates were studied. Quantitative screening appeared that Saccharomyces cerevisiae S4 was the highest chitinase producer specific activity 1.9 unit/mg protein. The yeast was culture in liquid and solid state fermentation media (SSF). Different plant obstanases were used for (SSF) with the chitine, while liquid media contained chitine with the diffrented nitrogen source. The favorable condition for chitinase producers were incubated at 30 ºC at pH 6 and 1% colloidal chitine.

This study represents an optical biosensor for early skin cancer detection using cysteine-cupped CdSe/CdS Quantum Dots (QDs). The study optimizes QD synthesis, surface, optical functionalization, and bioconjugation to enhance specificity and sensitivity for early skin cancer cell detection. The research provides insights into QD interactions with skin cancer biomarkers, demonstrating high-contrast, precise cellular imaging. Cysteine-capped CdSe/CdS absorption spectra reveal characteristic peaks for undamaged DNA, while spectral shifts indicate structural changes in skin-cancer-damaged DNA. Additionally, fluorescence spectra show sharp peaks for undamaged DNA and notable shifts and intensity variations when interacting with skin cancer. This