Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be

In this paper, some commonly used hierarchical cluster techniques have been compared. A comparison was made between the agglomerative hierarchical clustering technique and the k-means technique, which includes the k-mean technique, the variant K-means technique, and the bisecting K-means, although the hierarchical cluster technique is considered to be one of the best clustering methods. It has a limited usage due to the time complexity. The results, which are calculated based on the analysis of the characteristics of the cluster algorithms and the nature of the data, showed that the bisecting K-means technique is the best compared to the rest of the other methods used.

Image recognition is one of the most important applications of information processing, in this paper; a comparison between 3-level techniques based image recognition has been achieved, using discrete wavelet (DWT) and stationary wavelet transforms (SWT), stationary-stationary-stationary (sss), stationary-stationary-wavelet (ssw), stationary-wavelet-stationary (sws), stationary-wavelet-wavelet (sww), wavelet-stationary- stationary (wss), wavelet-stationary-wavelet (wsw), wavelet-wavelet-stationary (wws) and wavelet-wavelet-wavelet (www). A comparison between these techniques has been implemented. according to the peak signal to noise ratio (PSNR), root mean square error (RMSE), compression ratio (CR) and the coding noise e (n) of each third

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

Back ground: Oral lichen planus is a chronic inflammatory disease that affects the mucous membrane of the mouth. Several researchers suggest that oxidative stress is implicated in the pathogenesis of this disorder. It has been hypothesized that melatonin is powerful anti-oxidants and can easily cross the cell membrane and is considered a free radical scavenger of Hydroxid, Oxygen and Nitrogen dioxide, cortisol as a stress hormone and the immunoglobulin A as first line of defense and protection to the mucous membrane of the mouth are interrelated factors for the emergence of oral lichen planus. Aim of this study was to evaluate the level of melatonin, Cortisol and IgA in saliva of patients with oral lichen planus lesions in comparison with p

The idea of this research is based on presenting a study specialized with the rules and bases of the Iraqi methodological music composition of the Symphony Orchestra according to the construction of the musical compositions of great forms and which are characterized by the melody difference and variation within their musical fabric according to the method of distributing the instruments and their types within the orchestra. The researcher has been interested in studying this building based on what is written for him according to the stringed instruments arc instruments and bass, which is represented by the cello and the contrabass. It is possible through the analysis of the melody structure features of these instruments and the melody re

The Fylex extract exert a high inhibition effect against A . flavus growth on PDA medium, as the fungus growth was completely inhibited by 100% at a concentration of 0.2 and 0.3% of studied extract, while the lowest inhibition percentage (71%) was found at a concentration of 0.1%. Whereas magnesium oxide nanoparticles showed the highest inhibition ratio of A. flavus (100%) was detected at 0.2% and the lowest inhibition ratio (81.66%) was at concentration 0.5%. Moreover, the addition of G. lucidum powder to PDA medium with a concentration of 2.5 mg increased the inhibition rate of A. flavus growth which was 54.4%, while the lowest inhibition ration (18.22%) was found at a concentration of 1000 mg. The milky liquid (brocade milk) of Calotropi