Wasit Journal of Computer and Mathematics Science

An sophisticated medical technique used to diagnose illnesses and brain disorders including multiple sclerosis, Alzheimer\'s, and other neurological ailments is the ability to predict the biological age of the brain using MRI pictures. To do this, sophisticated algorithms and neural networks are used to scan MRI brain pictures in order to extract different brain properties, including cortical thickness and volume. The brain ages of individuals are determined by matching their characteristics against MRI imaging data collected from other patients. The research employs a new deep learning model named CNN-MRMR which combines features from the Minimum Redundancy Maximum Relevance (MRMR) feature selection approach and Convolutional Neural Network (CNN) technology. MRI images of human brains are initially processed by the convolutional network to extract age-related characteristics. The feature selection layer uses MRMR algorithm which identifies essential characteristics for a target variable while minimizing feature redundancy to select the optimal feature subset. The system employs a regression layer as the final stage to predict brain age by utilizing the selected characteristics. The proposed method for estimating individual brain age attained a prediction accuracy of 90.3%, outperforming results from comparable research studies.

In feature extraction, images or videos are analyzed to identify facial features before pinpointing the exact target. Significant progress has been made in the realm of feature extraction through deep learning. With deep learning technologies, developers have created algorithms for facial analysis and recognition that enhance accuracy and effectiveness. Consequently, programmers and developers have started to implement facial recognition technology in a wide range of applications to address these challenges. The identification process is particularly difficult due to the abundance of unstructured datasets. In real-world scenarios, identification continues to be quite challenging despite various methods proposed so far. This paper aims to analyze celebrity categorization based on deep features under different conditions. Most algorithms are influenced by these problematic factors, whether intentionally or unintentionally. Six datasets are utilized in this paper: the Open Famous People Dataset, Celeb Identification Dataset, Bollywood Celebrity Localized Dataset (170), Football Player's Dataset, Mini_LFW, and Nepali Celeb Localized Dataset. Most of these images feature celebrities including actors, singers, players, actresses, politicians, and social figures from diverse nationalities and countries. Therefore, this paper offers a combination of results from classification methods: Multilayer Perceptron Classifier (MLP), Decision Tree Classifier (DT), Bootstrap Aggregation Decision Tree Classifier (TreeBagger), Extreme Gradient Boosting Classifier (XGB), and Stochastic Gradient Descent Classification (SGD), alongside deep learning methods for feature extraction (VGGFace2 model pretraining and SENet-50 model architecture) across six different facial datasets. The results demonstrated that the highest accuracy (F-measure, Recall, and Precision) was achieved following the training and testing process in all datasets using the MLP and SGD classifiers.

In such an environment, an urgent requirement has been felt for systems that can adapt at runtime to
changing situations, as available computing environments are more and more dynamic and complex. The research
presented in this paper is about the use of Model Driven Engineering (MDE) for systematic support of managing
software adaptability. MDE advocates the use of models as core working entities in software development, allowing
you to raise the level of abstraction, transformation and consistency of the adaptive processes. The work presents a
full-fledged framework that combines MDE principles together with real-time monitoring, self-configuration
strategies, and the MAPE-K feedback loop.
By means of theoretical study and cross-domain case comparisons (smart environment, education, transportation,
and cloud computing), we show how MDE enables the structural, behavior, and parameter adaptation. The quality of
adaptation is evaluated based on a series of metrics, including adaptivity time, model similarity, and transformation
delay. The results substantiate the distinctive capability of MDE-based approaches for responsiveness and system
correctness in volatile environments and draw attention to important limitations with respect to tool maturity, runtime sync service and scale.
The paper concludes by discussing potential future endeavors to drive the field forward, including runtime
metamodel evolution, AI-driven model adaptation, and decentralized model-driven infrastructures. The work casts
MDE as a suitable and generalizable basis for the development of adaptive systems that exhibit resilience to
unanticipated failures, context-awareness, and autonomous adaptation

The concept of differential algebra has been initiated before many years ago. This research topic has inspired a lot of authors to its study with different algebraic structures such as rings or semi-rings. Their studies provided many good results in this field some of which by extending from previous works and others by introducing new notion in this direction. In this article a type of rings are called prime near-rings have been considered. In particular, we introduced the idea of generalized (theta,theta)-semi-derivation of prime near-rings and studied the commutativity of such types of rings by using this notion.

the early detection and successful treatment of skin cancers, a potent form of cancer, callsfor the use of
sophisticated diagnostic instruments. This study delves into the use of support vector machines (SVMs), to cope with
the inconsistencies occurring among skin lesions, by merging them with feature fusion techniques. SVMs are preferred
for this situation, as they are highly effective when it comes to the management of exceedingly dimensional data.
Initially, in order to train and enhance the diagnostic capacity of the SVM classifier, a single and all-inclusive single
dataset was generated through the analysis, identification and extraction of a wide variety of explanatory features
(including colour, texture and shape) from a dataset comprising 10000 dermatoscope skin lesion representations. This
was followed by the use of early and late fusion approaches, to generate an extensive dataset of descriptions, for
assessing the reliability of the SVM classifier. Finally, the accuracy, precision and recall of the SVM classifier were
ascertained by way of an objective dataset, comprising 25 dermatoscope representations of malignant and benign
lesions. The accuracy, precision and recall of the SVM classifier are supported by its capacity to distinguish 10 true
positives, 12 true negatives, three false positives and zero false negatives. As such, the SVM classifier can be
considered effective, for the early detection of skin cancers. The results from this investigation verify that the capacity
of SVMs, in terms of skin cancer diagnosis, is greatly improved with the utilization of feature fusion techniques. Also
verified through this undertaking, is the effectiveness of innovative computational procedures, for the delivery of
dependable medical diagnoses.