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  1. Article ; Online: IoT network security using autoencoder deep neural network and channel access algorithm

    Ali Saif Mohammed / Elameer Amer S. / Jaber Mustafa Musa

    Journal of Intelligent Systems, Vol 31, Iss 1, Pp 95-

    2021  Volume 103

    Abstract: Internet-of-Things (IoT) creates a significant impact in spectrum sensing, information retrieval, medical analysis, traffic management, etc. These applications require continuous information to perform a specific task. At the time, various intermediate ... ...

    Abstract Internet-of-Things (IoT) creates a significant impact in spectrum sensing, information retrieval, medical analysis, traffic management, etc. These applications require continuous information to perform a specific task. At the time, various intermediate attacks such as jamming, priority violation attacks, and spectrum poisoning attacks affect communication because of the open nature of wireless communication. These attacks create security and privacy issues while making data communication. Therefore, a new method autoencoder deep neural network (AENN) is developed by considering exploratory, evasion, causative, and priority violation attack. The created method classifies the transmission outcomes used to predict the transmission state, whether it is jam data transmission or sensing data. After that, the sensing data is applied for network training that predicts the intermediate attacks. In addition to this, the channel access algorithm is used to validate the channel for every access that minimizes unauthorized access. After validating the channel according to the neural network, data have been transmitted over the network. The defined process is implemented, and the system minimizes different attacks on various levels of energy consumption. The effectiveness of the system is implemented using TensorFlow, and the system ensures the 99.02% of detection rate when compared with other techniques.
    Keywords internet of things ; attacks ; network security ; autoencoder deep neural network ; channel access algorithm ; Science ; Q ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 303
    Language English
    Publishing date 2021-12-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Radiography image analysis using cat swarm optimized deep belief networks

    Elameer Amer S. / Jaber Mustafa Musa / Abd Sura Khalil

    Journal of Intelligent Systems, Vol 31, Iss 1, Pp 40-

    2021  Volume 54

    Abstract: Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, ... ...

    Abstract Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.
    Keywords radiography images ; statistical kolmogorov–simonov test ; cat swarm-optimized deep belief networks ; adadelta learning process ; Science ; Q ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Inhibitory behavior and adsorption of asparagine dipeptide amino acid on the Fe(111) surface.

    Hussein, Alaa Mohammed / Abbas, Zainab S / Kadhim, Mustafa M / Rheima, Ahmed Mahdi / Barzan, Maysm / Al-Attia, Laith Haitham / Elameer, Amer S / Hachim, Safa K / Hadi, Mohammed Abdul

    Journal of molecular modeling

    2023  Volume 29, Issue 5, Page(s) 162

    Abstract: Context: The inhibitory effect of asparagine (Asn) and its derivatives on iron (Fe) corrosion was studied by performing density functional theory (DFT) calculations. In this paper, the global and local reactivity descriptors of Asn in the protonated and ...

    Abstract Context: The inhibitory effect of asparagine (Asn) and its derivatives on iron (Fe) corrosion was studied by performing density functional theory (DFT) calculations. In this paper, the global and local reactivity descriptors of Asn in the protonated and neutral forms were evaluated. Also, the changes in reactivity were investigated when dipeptides were combined with Asn. Due to the increase in the reaction centers within their molecular structure, there was an enhancement in the inhibitory effect of these dipeptides. Moreover, the adsorption energies (E
    Methods: DFT computations were undertaken by employing the BIOVIA Material Studio with B3LYP-D3 functional and 6-31 + G* basis set. The theoretical evaluation of the inhibitory effect of asparagine (Asn) dipeptides, and the potential analysis of small peptides to protect against the corrosion of Fe, was done.
    MeSH term(s) Dipeptides/chemistry ; Amino Acids ; Asparagine/chemistry ; Adsorption ; Peptides
    Chemical Substances Dipeptides ; Amino Acids ; Asparagine (7006-34-0) ; Peptides
    Language English
    Publishing date 2023-04-28
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1284729-X
    ISSN 0948-5023 ; 1610-2940
    ISSN (online) 0948-5023
    ISSN 1610-2940
    DOI 10.1007/s00894-023-05555-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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