In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classifica

The new azo dye was synthesized via the reaction of the diazonium salt form of 3-aminophenol with 2-hydroxyquinoline. This dye was then used to access a series of complexes with the chlorides of manganese, iron, zinc, cadmium, and vanadium sulfate. The prepared ligand and its complexes were characterized by FT-IR spectroscopy, UV-visible spectroscopy, mass spectrometry, thermogravimetric analysis, differential scanning calorimeter, and microelemental analysis. Conductivity, magnetic susceptibility, metal content, and chlorine content of the complexes were also measured. The ligand and cadmium complex were identified using1H NMR and 13C NMR spectroscopy. The results showed that the shape of the ligand is a trigonal planner, and the c

حضر الليكاند (L) 1-فنيل-3-بردين-2-يل مثيل-ثايويوريا من تفاعل 2-أمينو مثيل بردين مع فنيل ايزوثايوسيانيت وبنسبة 1: 1 وشخص الليكاند بواسطة التحليل الدقيق للعناصر (C, H, N), الأشعة تحت الحمراء، الأشعة فوق البنفسجية–المرئية وطيف الرنين النووي المغناطيسي كما حضرت وشخصت معقدات أملاح بعض ايونات العناصر الثنائية التكافؤ (Co, Ni, Cu, Cd and Hg). استخدمت تقنية الأشعة تحت الحمراء، الأشعه فوق البنفسجية-المرئية, التوصيلية الكهربائية و الا

New Schiff bases derivatives [IV]a-e is prepared via condensation of Derythroascorbic acid with p-substituted aldehydes in dry benzene. To obtain these derivatives, the 5,6-O-isopropylidene-L-ascorbic acid[I] was chosen as starting material, compound prepared from the reaction of L-ascorbic acid as starting material. Compound[I] was prepared from the reaction of L-ascorbic acid with dry acetone in the presence of hydrogen chloride. The esterification of hydroxyl groups at C-2 and C-3 positions with excess ofethyl α –chloroacetate in the presence of sodium acetate produce acorresebonding ester [II] , which was condensed with hydrazine hydrate to give new hydrazide [III] . The new Schiff bases [IV]a-e were synthesized by reaction of acid h

In this research, a new 1, 3, 4-Thiadiazole derivatives have been synthesized by many heterocyclic reactions. Starting from (2, 5 – dimercapto -1, 3, 4-Thiadiazole) a variety of derivatives have been synthesis. Compound (1) was synthesized by the reaction of hydrazine hydrate with carbon disulphide in absolute ethanol. The compound (1) was reacted with 1, 2-dibromoethane in presence of alkali ethanol to give the compound (2). The compound (3) was formed from the reaction of compound (2) with hydrazine hydrate. Schiff base (4) was obtained by reacting of compound (3) with the compound (p-hydroxybenzaldehyde) in absolute ethanol. A variety of phenolic Schiff base (Methylolic, Etheric, and Epoxy) derivatives have been synthesized. Methylol

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in