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ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS
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The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

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Publication Date
Fri Jan 13 2023
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Kidney functions and electrolyte disturbance among Iraqi patients with bladder cancer
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المقدمة :يعد سرطان المثانة من بين أكثر أنواع السرطانات انتشارًا في جميع أنحاء العالم، حيث تم الإبلاغ عن 549,393 حالة جديدة في عام 2018 وما يقرب من ٪3 من جميع تشخيصات السرطان الجديدة و ٪2.1 من جميع وفيات السرطان ناتجة عن سرطان المثانة البولية. الاهداف: تهدف هذه الدراسة إلى استكشاف كفاءة وظائف الجهاز البولي بواسطة اجراء اختبارات وظائف الكلى ومستويات الايونات لمرضى سرطان المثانة. المنهجية: تم تحديد جميع الافراد الم

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Publication Date
Mon Jan 01 2018
Journal Name
Herald Namsca
STRUCTURE AND FUNCTIONS OF QUANTIFIER WORDS (IN ENGLISH, RUSSIAN AND JAPANESE)
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Abstract.. Quantitative characteristics of the surrounding world’s entities are expressed by the specific words – logical quantifiers. In a language, quantifiers are represented by the so-called quantifier words. Representing different degrees of the concentration of quantity, quantifier words in natural languages are positioned in a strict sequence – the quantifying scale. In terms of logic, it starts with nothingness (quantifier of meaningful nothingness), terminating at universality while in terms of language quantifier words, it is represented by the complex scale of quantifying words’ intensity. Logically speaking, this scale should start with the quantifier word ‘nothing’ to move all the way through increasing intensity ov

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Publication Date
Sat Oct 25 2025
Journal Name
Iet Networks
An Effective Technique of Zero‐Day Attack Detection in the Internet of Things Network Based on the Conventional Spike Neural Network Learning Method
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ABSTRACT<p>The fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature‐based datasets often perform poorly in ZD detection. A new technique for detecting zero‐day (ZD) attacks in IoT‐based Conventional Spiking Neural Networks (CSNN), termed ZD‐CSNN, is proposed. The model comprises three key levels: (1) Data Pre‐processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and t</p> ... Show More
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Publication Date
Sun Jun 06 2010
Journal Name
Baghdad Science Journal
Using Neural Network with Speaker Applications
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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty.

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Publication Date
Sun Sep 07 2008
Journal Name
Baghdad Science Journal
Hybrid Cipher System using Neural Network
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The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
Determination of the neutron refleetion coefficient as a function of reflector material
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This work presents a simple method for determination of the neutron reflection coefficient (n) as a function of different neutron reflector materials.A laboratory neutron source (Am-Be) with activity of 16 ci is employed with a (BF3) neutron detector. Am-BeThree types of reflector materials are used as samples, the thickness of each sample is (5cm).It is found that( ?7) is: -For polyethlyene = 0.818

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Publication Date
Fri Oct 01 2021
Journal Name
Nature Communications
CD177 modulates the function and homeostasis of tumor-infiltrating regulatory T cells
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Abstract<p>Regulatory T (Treg) cells are one of the major immunosuppressive cell types in cancer and a potential target for immunotherapy, but targeting tumor-infiltrating (TI) Treg cells has been challenging. Here, using single-cell RNA sequencing of immune cells from renal clear cell carcinoma (ccRCC) patients, we identify two distinct transcriptional fates for TI Treg cells, Fate-1 and Fate-2. The Fate-1 signature is associated with a poorer prognosis in ccRCC and several other solid cancers. CD177, a cell surface protein normally expressed on neutrophil, is specifically expressed on Fate-1 TI Treg cells in several solid cancer types, but not on other TI or peripheral Treg cells. Mechanistically, blocking CD</p> ... Show More
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Publication Date
Sun Mar 01 2020
Journal Name
Sustainable Chemistry And Pharmacy
A sustainable approach to utilize olive pips for the sorption of lead ions: Numerical modeling with aid of artificial neural network
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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

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Publication Date
Sat Sep 01 2012
Journal Name
2012 8th International Conference On Wireless Communications, Networking And Mobile Computing
Performance Evaluation of Location Management in GSM Networks
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