The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.
Background:-The Modified Alvarado Scoring
System (MASS) has been reported to be a cheap
and quick diagnostic tool in patients with acute
appendicitis. However, differences in diagnostic
accuracy have been observed if the scores were
applied to various populations and clinical settings.
Objectives:- The purpose of this study was to
evaluate the diagnostic value of Modified Alvarado
Scoring System in patients with acute appendicitis
in our setting.
Methods:-one hundre twenty eight patients, were
included in this study, admitted to Al-Kindy
teaching hospital from June 2009 to June 2010.
Patients’ age ranged from 8 to 56 years (21±10)
they were divided into three groups; paediatrics,
child bear
Background:-The Modified Alvarado Scoring System (MASS) has been reported to be a cheap and quick diagnostic tool in patients with acute appendicitis. However, differences in diagnostic accuracy have been observed if the scores were applied to various populations and clinical settings.
Objectives:- The purpose of this study was to evaluate the diagnostic value of Modified Alvarado Scoring System in patients with acute appendicitis in our setting.
Methods:-one hundre twenty eight patients ,were included in this study, admitted to Al-Kindy teaching hospital from June 2009 to June 2010. Patients’ age ranged from 8 to 56 years (21±10) they were divided into three groups; paediatrics, child bearing age females & adult males,. MAS
In this research, we did this qualitative and quantitative study in order to improve the assay of aspirin colorimetrically using visible spectrophotometer. This method depends on aqueous hydrolysis of aspirin and then treating it with the ferric chloride acidic solution to give violet colored complex with salicylic acid, as a result of aspirin hydrolysis, which has a maximum absorption at 530nm. This procedure was applied to determine the purity of aspirin powder and tablet. The results were approximately comparative so that the linearity was observed in the high value of both correlation coefficient (R= 0.998) and Determination Coefficient or Linearity (R2= 0.996) while the molar absorpitivity was 1.3× 103 mole
The important aspect of this unconventional approach is that eco-friendly, commercially available and straight forward method was used to prepared Silver Nanoparticles by using AgNO3 and curcumin solution as agent factor. The (TEM), (XRD), and (FTIR) was used to characterise these silver nanoparticles (AgNPs). Two types of bacterial isolates were used to indicate the antibacterial activity silver nanoparticles which prepared by curcumin solution, Gram negative like (Escherichia Coli E. Coli), & Gram positive (Stapha Urous). The results exhibit that silver nanoparticles synthesized by curcumin solution has effective antibacterial activities.
Fatigue cracking is the most common distress in road pavement. It is mainly due to the increase in the number of load repetition of vehicles, particularly those with high axle loads, and to the environmental conditions. In this study, four-point bending beam fatigue testing has been used for control and modified mixture under various micro strain levels of (250 μƐ, 400 μƐ, and 750 μƐ) and 5HZ. The main objective of the study is to provide a comparative evaluation of pavement resistance to the phenomenon of fatigue cracking between modified asphalt concrete and conventional asphalt concrete mixes (under the influence of three percentage of Silica fumes 1%, 2%, 3% by the weight of asphalt content), and (chan
... Show MoreIn this paper, the Azzallini’s method used to find a weighted distribution derived from the standard Pareto distribution of type I (SPDTI) by inserting the shape parameter (θ) resulting from the above method to cover the period (0, 1] which was neglected by the standard distribution. Thus, the proposed distribution is a modification to the Pareto distribution of the first type, where the probability of the random variable lies within the period The properties of the modified weighted Pareto distribution of the type I (MWPDTI) as the probability density function ,cumulative distribution function, Reliability function , Moment and the hazard function are found. The behaviour of probability density function for MWPDTI distrib
... Show MoreAphelenchus avenae was isolated from the wheat crown in Summel distract- Duhok, Kurdistan region-Iraq infected by a crown rot disease which is caused by Fusarium spp; wheat's crown culturing on Potato Dextrose Agar (PDA) and incubating at 25°C A. avenae was found associated with fungal culture which meant that fungal nematode was parasitic on crown rot fungi on wheat crown, this species was described for the first time in Iraq.
Fungal Nematode incubated with Fusarium graminearum, F. oxysporum and Verticillium dahliae reproduce in both solid and liquid media, best results of nematode reproduction were recorded on F. graminearum followed by F. oxy
... Show MoreImitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co
... Show MoreTo expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo
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