Osteoarthritis (OA) is recognized as a main public health difficult. It is one of the major reasons of reduced function that diminishes quality of life worldwide. Osteoarthritis is a very common disorder affecting the joint cartilage. As there is no cure for osteoarthritis, treatments currently focus on management of symptoms. Pain relief, improved joint function, and joint stability are the main goals of therapy. The muscle weakness and muscle atrophy contribute to the disease process. So, rehabilitation and physiotherapy were often prescribed with the intention to alleviate pain and increase mobility. Medical therapy provides modest benefits in pain reduction and functional improvement; however, non-steroidal anti-inflammatory dru

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

The article characterizes metaphors and comparisons as a means of rethinking the phenomena of the surrounding world; two directions are named within which metaphors and comparisons are studied: 1) the linguo-classification direction combines several classifications of metaphors and comparisons, which are basically focused on comparing two denotations through the meanings of word forms that are part of the trope; 2) the theoretical and conceptual direction is associated with the concept of conceptual displacement and is the basis of the theoretical and methodological study of metaphor and comparison, i.e. the modern theory of metaphor is being formed; The main attention is focused on the comparative concept of Aristotle, which postul

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

Let R be a commutative ring with identity and let M be a unitary left R-module. The purpose of this paper is to investigate some new results (up to our knowledge) on the concept of semi-essential submodules which introduced by Ali S. Mijbass and Nada K. Abdullah, and we make simple changes to the definition relate with the zero submodule, so we say that a submodule N of an R-module M is called semi-essential, if whenever N ∩ P = (0), then P = (0) for each prime submodule P of M. Various properties of semi-essential submodules are considered.

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

  Necessary and sufficient conditions for the operator equation I AXAX n  * , to have a real positive definite solution X are given. Based on these conditions, some properties of the operator A as well as relation between the solutions X andAare given.