Inclusion Properties for Certain Subclasses of Uniformly P-Valent Analytic Functions Involving Linear Operator

In this paper, the authors study some inclusion results for new subclasses of b-uniformly -valent functions in the open unit disc defined by differ-integral operator and some results of certain integral operator are also obtained

Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction

سرطان الثدي يعتبر واحد من الامراض القاتلة الشائعة بين النساء في جميع أنحاء العالم. والتشخيص المبكر لسرطان الثدي الكشف المبكر من أهم استراتيجيات الوقاية الثانوية. نظرًا لاستخدام التصوير الطبي على نطاق واسع في تشخيص العديد من الأمراض المزمنة ومراقبتها، فقد تم اقتراح العديد من خوارزميات معالجة الصور على مر السنين لزيادة مجال التصوير الطبي بحيث تصبح عملية التشخيص أكثر دقة وكفاءة. تقدم هذه الدراسة خوارزمية جديدة لاستخراج الخواص العميقة من نوعين من صور الرنين المغناطيسي T2W-TSE و STIR MRI كمدخلات للشبكات العصبية العميقة المقترحة والتي تُستخدم لاستخراج الخواص للتمييز بين فحوصات التصوير بالرنين المغناطيسي للثدي المرضية والصحية. في هذه الخوارزمية، تتم معالجة فحوصات التصوير بالرنين المغناطيسي للثدي مسبقًا قبل خطوة استخراج الخواص لتقليل تأثيرات الاختلافات بين شرائح التصوير بالرنين المغناطيسي، وفصل الثدي الايمن عن الايسر، بالإضافة الى عزل خلفية الصور. وقد كانت أقصى دقة تم تحقيقها لتصنيف مجموعة بيانات تضم 326 شريحة تصوير بالرنين المغناطيسي للثدي 98.77٪. يبدو أن النموذج يتسم بالكفاءة والأداء ويمكن بالتالي اعتباره مرشحًا للتطبيق في بيئة سريرية.Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature extraction step to enhance and preserve the fine details of the breast MRI scans boundaries by using fractional integral entropy FIE algorithm, to reduce the effects of the intensity variations between MRI slices, and finally to separate the right and left breast regions by exploiting the symmetry information. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, all extracted features significantly improves the performance of the LSTM network to precisely discriminate between pathological and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 326 T2W-TSE images and 326 STIR images is 98.77%. The experimental results demonstrate that FIE enhancement method improve the performance of CNN in classifying breast MRI scans. The proposed model appears to be efficient and might represent a useful diagnostic tool in the evaluation of MRI breast scans

Time-Space Fractional Heat Equation in the Unit Disk

We will study a maximal solution of the time-space fractional heat equation in complex domain. The fractional time is taken in the sense of the Riemann-Liouville operator, while the fractional space is assumed in the Srivastava-Owa operator. Here we employ some properties of the univalent functions in the unit disk to determine the upper bound of this solution. The maximal solution is illustrated in terms of the generalized hypergeometric functions

On Generalized Fractional Differentiator Signals

By employing the generalized fractional differential operator, we introduce a system of fractional order derivative for a uniformly sampled polynomial signal. The calculation of the bring in signal depends on the additive combination of the weighted bring-in of N cascaded digital differentiators. The weights are imposed in a closed formula containing the Stirling numbers of the first kind. The approach taken in this work is to consider that signal function in terms of Newton series. The convergence of the system to a fractional time differentiator is discussed

Effect of Different Plant Growth Regulators on Callus Induction from Seeds of Chickpea (Cicer arietinum L.)

The present study was undertaken to develop a reproducible protocol for efficient in vitro callus initiation of chick pea (Cicer arietinum L.). The main objectives of this present study were to develop the optimal concentrations and combination of auxin and cytokinin for optimized callus induction from seeds as explants. Callus induction was initiated from seeds on MS media supplement, which varied according to the plant growth regulators treatment. Among the growth regulator combinations the highest rate of callus induction (85%) was observed in MS medium containing 2 mg L -1 of 2,4-Dichlorophenoxyacetic Acid (2,4-D), 2 mg L -1 Benzylaminopurine (BAP) showed higher percentage ( 63% ) of callus formation than 1- Naphthaleneacetic acid (NAA), which produced 49% of callus. There were significant differences in percentage of calli fresh/dry weights (g/jar) on the different initiation ( seven) medium used were the MS+2,4-D, MS+2,4-D +NAA+ BAP and MS+ BAP had the highest fresh/dry weights (g/jar) in both induction medium

MRI brain classification using the quantum entropy LBP and deep-learning-based features

Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy di erence defined in terms of Marsaglia formula (usually used to describe two di erent figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.N/