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. This work modernize the feedforward neural network, so the secret message will be encrypted by unsupervised neural network method to get the cipher text that can be decrypted using the same network to get the original text. 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 lengths. In this work, the key is the final weights that are obtained from the learning process within the neural network stage, So the work can be represented as an update or development for using the neural network to enhance the security of text. As a result for a powerful design, the resulted cipher system provides a high degree of security which satisfies the data confidentially which is the main goal of the most cryptography systems.
The prostheses sockets use normally composite materials which means that their applications may be related with the human body. Therefore, it was very necessary to improve the mechanical properties of these materials. The prosthetic sockets are subjected to varying stresses in gait cycle scenario which may cause a fatigue damage. Therefore, it is necessary or this work to modify the fatigue behavior of the materials used for manufacturing the prostheses sockets. In this work, different Nano particle materials are used to modify the mechanical properties of the composite materials, and increase the fatigue strength. By using an experimental technique, the effect of using different volu
A Raman spectroscopy method was optimised to examine the chemical changes of aspirin tablets after interaction with helium temperatures. Several aspirin tablets were exposed to plasma-assisted desorption ionisation flame for different times (10, 30, 50, 60, 180 and 300s) and then analysed by Raman spectroscopy using optimal conditions. The changes in chemistry between exposed and fresh (without exposure to plasma) tablets were compared. The vibrational peaks of the aspirin molecule in the Raman spectrum were identified by checking the peak position. The results showed clear spectra with increases in intensity of vibrational peaks until 30s, whereas no spectra were measured for the exposed tablets to plasma flame after 50s. It can, the
... Show MoreA field-pilot scale slow sand filter (SSF) was constructed at Al-Rustamiya Sewage Treatment Plant (STP) in Baghdad city to investigate the removal efficiency in terms of Biochemical Oxygen Demand (BOD5), Chemical oxygen demand (COD), Total Suspended Solids (TSS) and Chloride concentrations for achieving better secondary effluent quality from this treatment plant. The SSF was designed at a 0.2 m/h filtration rate with filter area 1 m2 and total filter depth of 2.3 m. A filter sand media 0.35 mm in size and 1 m depth was supported by 0.2 m layer of gravel of size 5 mm. The secondary effluent from Al-Rustamiya STP was used as the influent to the slow sand filter. The results showed that the removal of BOD5, COD, TSS, and Chloride were
... Show MoreDeep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
... Show MoreKeys for 22 species representing 10 genera of Thripidae were provided collection of
samples carried out during 1999-2001 in different localities in the middle of Iraq. Of them
four species are described as new to science, Frankliniella megacephala sp. nov; Retithrips
bagdadensis sp. nov; Chirothrips imperatus sp. nov; Taeniothrips tigridis sp. nov; Another
fourteen species are recorded for the first time in Iraq; Thrips meridionalis (Pri.);
Microcephalothrips abdominils (Crawford Scolothrips sexmaculatus (Pergande),);Scolothrips
pallidus (Beach); Scritothrips mangiferae Pri.; Frankliniella tritici Bagnall; Frankliniella
schultzie Trybom; Frankliniella unicolor Morgan; Retithrips aegypticus Marchal; Retithrips
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