Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

Global concerns are rising due to complications associated with the use of chemical agents and antibiotic resistance. Consequently, research focus has shifted towards the quest for effective agents of biological origin. The aim of the present study was to assess the antioxidant and antimicrobial potentials of aqueous and organic extracts derived from various parts of Alcea kurdica. Different parts of A. kurdica were obtained and prepared into leaf, flower and root powders. The powders were extracted with aqueous and organic solvents. The antimicrobial activity of these extracts was assessed against bacterial pathogens using the agar well-diffusion assay. Additionally, the antioxidant effects of the extracts were evaluated using the

  This study was done to investigate the antibacterial effect of the three types of Lawsonia inermis linn (henna) leaf extracts (water, methanol and chloroform) in different concentrations (40, 80, 120) mg/ml against four strains of bacteria (Staphyllococus aureus, Bacillus subtilis, Pseudomonos aerogenosa and Eschorichia coli) in vitro using ager well diffusion method. Water extract showed the highest antibacterial activity, followed by  methanol extract, while the chloroform extract showed the lowest activity. The maximum inhibition zone of water extract was observed against , Pseudomonos aerogenosa (25mm) in the concentration (120) mg/ml, while the minimum zone of inhibition (9mm) was in Bacillus subtilis in the same concent

The use of medicinal plant preparations to clean and disinfect root canal infection is gaining popularity. The aim of this study was to evaluate the bioactive composition of leaf extracts of Moringa oleifera plants cultivated in Iraq (specifically Baghdad) and their antimicrobial activity against selected root canal pathogens for potential application in endodontic treatment. Materials and Methods. Moringa leaf extracts were prepared either through cold maceration or warm digestion techniques to perform an ethanolic or aqueous extraction, respectively. Phytochemical detection was performed before thin layer chromatography (TLC) and high-performance liquid chromatography (HPLC) to measure flavonoids and phenolic compounds within both

A field-pilot scale slow sand filter (SSF) was constructed at Al-Rustamiya Sewage Treatment Plant (STP) in Baghdad city to investigate the removal efficiency in terms of Biochemical Oxygen Demand (BOD5), Chemical oxygen demand (COD), Total Suspended Solids (TSS) and Chloride concentrations for achieving better secondary effluent quality from this treatment plant. The SSF was designed at a 0.2 m/h filtration rate with filter area 1 m2 and total filter depth of 2.3 m. A filter sand media 0.35 mm in size and 1 m depth was supported by 0.2 m layer of gravel of size 5 mm. The secondary effluent from Al-Rustamiya STP was used as the influent to the slow sand filter. The results showed that the removal of BOD5, COD, TSS, and Chloride were

One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues