We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

Vitamins play an important role in the human health, and thus they are the kind of major nutrients in the body. Chemical products perform numerous physiological functions and can jeopardize health jointly in their absence and surplus. Therefore, it is necessary to establish methods for observation vitamin levels in various molds. In this review paper, the most methods of determination used are high performance liquid chromatography (HPLC), spectrophotometric and potentiometric techniques by listed the value of : slope, linear range, correlation coefficient, detection limit, the max of wavelength and PH and compared with these methods.

In this review of literature, the light will be concentrated on the local drugs delivery systems for treating the periodontal diseases. Principles, types, advantages and indications of each type will be discussed in this paper.

This research takes up address the practical side by taking case studies for construction projects that include the various Iraqi governorates, as it includes conducting a field survey to identify the impact of parametric costs on construction projects and compare them with what was reached during the analysis and the extent of their validity and accuracy, as well as adopting the approach of personal interviews to know the reality of the state of construction projects. The results showed, after comparing field data and its measurement in construction projects for the sectors (public and private), the correlation between the expected and actual cost change was (97.8%), and this means that the data can be adopted in the re

Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

Sub-threshold operation has received a lot of attention in limited performance applications.However, energy optimization of sub-threshold circuits should be performed with the concern of the performance limitation of such circuit. In this paper, a dual size design is proposed for energy minimization of sub-threshold CMOS circuits. The optimal downsizing factor is determined and assigned for some gates on the off-critical paths to minimize the energy at the maximum allowable performance. This assignment is performed using the proposed slack based genetic algorithm which is a heuristic-mixed evolutionary algorithm. Some gates are heuristically assigned to the original and the downsized design based on their slack time determined by static tim

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri